Temperature Change Trend Research Articles

Global Temperature Anomaly Forecast: A Comparative Analysis of ARIMA and ETS Models

Global temperature change is a central topic in climate change research, and predicting future temperature anomalies is a key to developing climate risk mitigation plans. To better predict the long-term trend of global temperature change, this paper uses the NASA GISTEMP dataset from 1880 to the present. It employs two widely used models for time-series forecasting, the ARIMA, and the ETS models, to model and forecast global temperature anomalies. Through model fitting and comparative analysis, this paper demonstrates the superior performance of the ARIMA model in dealing with the prediction of long-term trends in global temperature. It points out that the ETS model exhibits relatively low errors in highly fluctuating data. Considering AIC, BIC, MSE, and other indicators, the ARIMA model is more stable in long-term trend forecasting, especially when dealing with time series without obvious seasonal components.

Analysis of air temperature change trends and their impacts in Australia

Global climate change has become a major challenge of our time, affecting ecosystems, weather patterns, and human activities across the globe. In this paper, we analyze the causes and effects of climate change by looking at Australia, in particular, summer temperature trends in the Melbourne region over the past 20 years. By examining historical data and considering the current situation, this paper makes recommendations to address these challenges. The relevant data shows a general increase in average summer temperatures in Melbourne and across Australia from 2003 to 2023. The main factors contributing to these fluctuations include global climate change, human activity, and urbanization. Continued temperature increases are primarily caused by global warming, exacerbated by increased greenhouse gas emissions from human activities and the urban heat island effect. The impacts of climate change on Melbourne and Australia are multifaceted, affecting public health, water management, energy demand, ecosystems, and agriculture. After analyzing Australia's overall climate, this paper proposes measures to improve it, such as reducing greenhouse gas emissions, increasing urban resilience, improving public health security, protecting and restoring ecosystems, and promoting scientific research and education.

Effect of pouring temperature and rate of glass on thermal shock behavior of nuclear waste container

In this paper, the injection process of glassy nuclear waste is simulated using the ANSYS Workbench simulation platform and the birth and death element method. The results show that the temperature change trend obtained by simulating the injection process of solidification container by using the birth and death element method is consistent with the literature results. It is feasible to simulate the injection process of glassy nuclear waste by using the birth and death element method of ANSYS. The thermal calculation results show that the container above the liquid level will produce large stress due to mutual restraint, and the container will undergo plastic deformation when heated during the injection process. As the pouring temperature increases from 1000°C to 1200°C, the maximum stress at the upper head increases from 212.88 to 232.46 MPa. The deformation degree of the solidified container increases with the increase of pouring temperature during the injection process. With the increase of pouring rate from 0.025 to 0.04 kg/s, at the end of injection, the maximum stress of the upper head decreases from 226.96 to 214.71 MPa with the increase of pouring rate. The overall deformation degree of the solidified container increases with the increase of the pouring rate.

Analysis of the Trends of Change in Temperature and Precipitation and their Impact on Water in Anbar Governorate (1980-2023)

Objectives: The research aims to study the changes in temperature and rainfall from 1980 to 2023 using the Mann-Kendall test model to analyze trends in temperature and rainfall changes in Al-Anbar Governorate. This model helps to detect and assess the presence of statistically significant trends in time series data by comparing the relative rankings of values over time. Method: The study relied on both an inductive-analytical approach and a deductive approach based on scientific methods to select information and data, ensuring a comprehensive examination by combining climatic elements and hydrological characteristics. Quantitative and statistical methods were applied to achieve the research objectives. Results: The study found that average temperatures in Al-Anbar have significantly increased. Al-Qaim station recorded the lowest change at 0.924°C for all study years and 0.21°C per decade, while Rutba station recorded the highest at 1.848°C and 0.42°C per decade. For rainfall, Haditha station saw the lowest decrease at -47.828 mm, while Al-Qaim station experienced the largest reduction at -58.036 mm. This shift has led to a 40% decline in rainfall and a growing water deficit in the Euphrates River, projected to reach 2403.8 million cubic meters by 2040. Conclusion: The research concluded that it is essential to intensify local and international efforts to mitigate the risks of global warming and its severe consequences, which manifest in reduced rainfall and increased drought waves.

Evaluation of wound temperature monitoring at various anatomical sites in the management of patients with diabetic foot undergoing microcirculation reconstruction

ObjectiveThis study aims to assess the significance of monitoring temperature change trends at various wound sites in the healing process of diabetic foot ulcers after microcirculation reconstruction surgery.MethodsA retrospective analysis was conducted on individuals with diabetic foot ulcers who had been admitted to the Department of Orthopedics at the Second Hospital of Shanxi Medical University between July 2020 and February 2022. Temperature changes were regularly monitored at the center of the wound and the distal tibia of the ipsilateral lower leg to assess microcirculatory blood perfusion. Wound, ischemia, and foot infection (WIFi) grading was performed at admission and the final follow-up was to determine the value of temperature monitoring at various sites. Additionally, the formation of collateral microarterial vessels was monitored to determine their consistency with the observed trends in temperature differences. Follow-up assessments included the recurrence of ulcers, development of ulcers at different locations, re-amputation of the toe or limb, and diabetes-related mortality.ResultsA total of 29 patients were included in the follow-up, with an average age of 57.14 ± 14.75 years and a follow-up period of 9.79 ± 4.13 months. Following microcirculation reconstruction surgery, as the microvascular network formed, the temperature difference between the center of the wound and the distal tibia on the same side gradually decreased, with no statistical difference observed at 4 weeks postoperatively. At both admission and the final follow-up, there was a significant reduction in the wound (W) and ischemia (I) grades within the WIFi classification. The temperature at the wound center showed progressive improvement as collateral microarterial vessels developed. During the follow-up period, there were 2 cases of ulcer recurrence, 1 case of an ulcer appearing at a different location, no cases of re-amputation of the toe or limb, and 2 diabetes-related fatalities.ConclusionSkin temperature monitoring offers a direct and reliable indication of microcirculatory blood perfusion. Its simplicity and cost-effectiveness make it a valuable tool for widespread use in evaluating wound healing following microcirculation reconstruction surgery.

Viscoelastic study of cement and emulsified asphalt mortar under temperature variations based on a novel variable-order fractional Maxwell model

Viscoelastic study of cement and emulsified asphalt mortar under temperature variations based on a novel variable-order fractional Maxwell model

Long–period trends in water temperature changes in the northern part of the Atlantic Ocean from ocean reanalysis data

The results of assessing long–period changes in water temperature in the North Atlantic Ocean (0°–70° N, 8°–80° W) based on data from ocean reanalyses and objective analyses for the periods 1961– 2011 and 1980–2011 are presented. The obtained estimates are based on the application of a nonparametric method of regression analysis (quantile regression) to the monthly ocean temperature for a quantile value of 0.5. During the period 1961–2011 warming was mainly observed in the upper 400 m layer in the region from the equator to 70° N. Over this 51-year period, the increase in the median monthly ocean temperature averaged over the analyzed water area ~0.5°C, and in the Gulf Stream–North Atlantic Current system ~1°C. During the period 1980–2011 warming in the North Atlantic Ocean mainly occurred in the upper 1 km layer at high latitudes (50°–65° N). Over this 32-year period, the increase in the median monthly ocean temperature in the subpolar gyre in the upper 400 m layer was ~1°C.

Study on Multi-Parameter Variation of Smoke Flow in Inclined Roadway Fire

ABSTRACT Based on the fluid similarity theory, a “Mine inclined roadway fire simulation experimental system” with similarity ratio of 1:20 is established. The temperature, velocity, density and dynamic pressure of fire smoke under different inlet wind speed (0.5 m/s, 1.0 m/s, 1.5 m/s) were tested and analyzed, and the distribution of temperature, velocity, density and dynamic pressure of inclined roadway during fire was simulated and analyzed by Ansys numerical simulation software. The research shows that: The higher the inlet wind speed, the faster the heat diffusion and the temperature field tends to be flat and long, the wind speed changes in advance and the change is drastic, the gas density decreases, the dynamic pressure first decreases and then increases and then tends to stabilize, but the change trend of temperature, wind speed, gas density and pressure in the overall combustion process remains unchanged. The numerical analysis results are in good agreement with the experimental results. By studying the change law of smoke temperature, velocity, density and dynamic pressure of coal mine inclined roadway fire, the theory of coal mine roadway fire is further perfected, and the level of coal mine fire prevention and rescue is improved.

Improving the ERA5-Land Temperature Product through a Deep Spatiotemporal Model That Uses Fused Multi-Source Remote Sensing Data

Temperature is a crucial indicator for studying climate, as well as the social and economic changes in a region. Temperature reanalysis products, such as ERA5-Land, have been widely used in studying temperature change. However, global-scale temperature reanalysis products have errors because they overlook the influence of multiple factors on temperature, and this issue is more obvious in smaller areas. During the cold months (January, February, March, November, and December) in the Yellow River Basin, ERA5-Land products exhibit significant errors compared to temperatures observed by meteorological stations, typically underestimating the temperature. This study proposes improving temperature reanalysis products using deep learning and multi-source remote sensing and geographic data fusion. Specifically, convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) capture the spatial and temporal relationships between temperature, DEM, land cover, and population density. A deep spatiotemporal model is established to enhance temperature reanalysis products, resulting in higher resolution and more accurate temperature data. A comparison with the measured temperatures at meteorological stations indicates that the accuracy of the improved ERA5-Land product has been significantly enhanced, with the mean absolute error (MAE) reduced by 28.7% and the root mean square error (RMSE) reduced by 25.8%. This method obtained a high-precision daily temperature dataset with a 0.05° resolution for cold months in the Yellow River Basin from 2015 to 2019. Based on this dataset, the annual trend of average temperature changes during the cold months in the Yellow River Basin was analyzed. This study provides a scientific basis for improving ERA5-Land temperature reanalysis products in the Yellow River Basin and offers theoretical support for climate change research in the region.

Alterations in nasal airflow and air conditioning after endoscopic nasopharyngectomy for recurrent nasopharyngeal carcinoma: a pilot computational fluid dynamics study

Endoscopic nasopharyngectomy represents a significant intervention for recurrent nasopharyngeal carcinoma (NPC). Various surgical techniques, including transnasal and transoral approaches, are employed. However, the impact of these procedures on nasal airflow dynamics is not well understood. This computational fluid dynamics (CFD) study aimed to investigate alterations in nasal airflow and air conditioning following endoscopic nasopharyngectomy. A 55-year-old male patient with recurrent NPC was selected, whose CT data were utilized for image reconstruction. A preoperative model and two postoperative models, including the transnasal and transoral approach models, were established. The airflow patterns and various CFD parameters were analyzed. In the postoperative models, the high-speed airflow went along the soft palate and into the nasopharyngeal outlet, and there was the low-speed turbulence in the expanded nasopharyngeal cavity. Compared to the preoperative model, the postoperative models exhibited reductions in surface-to-volume ratio, nasal resistance, airflow velocity and proportion of high wall shear stress regions in nasopharynx. The changing trends of nasopharyngeal air temperature and humidity in the preoperative and transoral models were consistent. The heating and humidification efficiency decreased in the transnasal model compared to the transoral model. The endoscopic nasopharyngectomy for recurrent NPC affects the nasal airflow and warming and humidification function. The transoral approach has less influence on aerodynamics of the upper airway compared to the transnasal approach. From a CFD perspective, the endoscopic nasopharyngectomy does not increase the risk of postoperative complications, including the empty nose syndrome and the carotid blowout syndrome.

Exploring climate shifts in the Ganga-Brahmaputra basin based on rainfall and temperature variability.

Climate change has a significant impact on the Ganga-Brahmaputra (GB) basin, the major food belt of India, which frequently experiences flooding and varied incidences of drought. The current study examines the changing trend of rainfall and temperature in the GB basin over a period of 30years to identify areas at risk with an emphasis on the Paris Agreement's mandate to keep increasing temperatures below 2°C. The maximum temperature anomaly in the middle Ganga plains recorded an increase of more than 1.5°Cyear-1 in 1999, 2005, and 2009. Some extreme events were observed in the Brahmaputra basin during 1999, 2009, and 2010, where a prominent temperature increase of 1.5°Cyear-1 was observed. The minimum temperature revealed an increasing trend for the G-B basin with an anomalous increase of 0.04 to 0.06°Cyear-1. The rainfall variability across the Ganga basin shows a rising tendency over the lower Ganga region while the Brahmaputra basin showed a downward trend. To identify the statistical relation between the Global climatic oscillations and regional climate, Standardized Precipitation Index (SPI) and Niño 3.4 were used. The wet and dry period estimation shows a rise in flood conditions in the Ganga basin whereas, in the Brahmaputra basin, an increase in drought frequency was observed. The correlation based on Niño 3.4 and SPI3 presents a negative relation for the monsoon season in the G-B basin revealing a situation of drought occurrence (SPI3 below 0) with increased Nino 3.4 values (El Niño above + 0.4C).

Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections

This study addresses the critical issue of drought zoning in Canada using advanced deep learning techniques. Drought, exacerbated by climate change, significantly affects ecosystems, agriculture, and water resources. Canadian Drought Monitor (CDM) data provided by the Canadian government and ERA5-Land daily data were utilized to generate a comprehensive time series of mean monthly precipitation and air temperature for 199 sample locations in Canada from 1979 to 2023. These data were processed in the Google Earth Engine (GEE) environment and used to develop a Convolutional Neural Network (CNN) model to estimate CDM values, thereby filling gaps in historical drought data. The CanESM5 climate model, as assessed in the IPCC Sixth Assessment Report, was employed under four climate change scenarios to predict future drought conditions. Our CNN model forecasts CDM values up to 2100, enabling accurate drought zoning. The results reveal significant trends in temperature changes, indicating areas most vulnerable to future droughts, while precipitation shows a slow increasing trend. Our analysis indicates that under extreme climate scenarios, certain regions may experience a significant increase in the frequency and severity of droughts, necessitating proactive planning and mitigation strategies. These findings are critical for policymakers and stakeholders in designing effective drought management and adaptation programs.

Research on CNN-LSTM-Attention based surface temperature prediction model for fermented grains

The prediction of the temperature on the surface of the fermented grains during the steaming process holds significant importance in improving the alcohol yield and quality. To accurately forecast the temperature variations on the surface of the fermented grains, a predictive model based on CNN-LSTM-Attention is proposed. Leveraging convolutional neural networks, latent features of the temperature data on the fermented grains surface are extracted. These extracted feature vectors, representing a time series, are then inputted into a long short-term memory network to further capture the temporal characteristics of the sequence. Finally, employing an attention mechanism, the influential features are highlighted to achieve precise prediction of the fermented grains surface temperature. Historical temperature data from the steaming process of the fermented grains is utilized, and experimental comparisons are conducted with other neural network prediction models. The results demonstrate that the CNN-LSTM-Attention model achieves optimal root mean square error of 1.282 and average absolute error of 0.647, surpassing other models. The experimental findings substantiate the superior accuracy of the CNN-LSTM-Attention model in forecasting the changing trend of the fermented grains surface temperature.

Temperature‐Activated Near‐Infrared‐II Fluorescence and SERS Dynamic‐Reversible Probes for Long‐Term Assessment of Osteoarthritis In Vivo

AbstractThe abnormal fluctuation of temperature in vivo usually reflects the progression of inflammatory diseases. Noninvasive, real‐time, and accurate monitoring and imaging of temperature variation in vivo is advantageous for guiding the early diagnosis and treatment of disease, but it remains difficult to achieve. Herein, we developed a temperature‐activated near‐infrared‐II fluorescence (NIR‐II FL) and surface‐enhanced Raman scattering (SERS) nanoprobe for long‐term monitoring of temperature changes in rat arthritis and timely assessment of the status of osteoarthritis. The thermosensitive polymer bearing NIR‐II FL dye was grafted onto the surface of nanoporous core‐satellite gold nanostructures to form the nanoprobe, wherein the nanoprobe contains NIR‐II FL and Raman reference signals that are independent of temperature change. The ratiometric FL1150/FL1550 and S1528/S2226 values of the nanoprobe exhibited a reversible conversion with temperature changes. The nanoprobe accurately distinguishes the temperature variations in the inflamed joint versus the normal joint in vivo by ratiometric FL and SERS imaging, allowing for an accurate diagnosis of inflammation. Meanwhile, it can continuously monitor fluctuations in temperature over an extended period during the onset and treatment of inflammation. The tested temperature change trend could be used as an indicator for early diagnosis of inflammation and real‐time evaluation of therapeutic effects.

Temperature-Activated Near-Infrared-II Fluorescence and SERS Dynamic-Reversible Probes for Long-Term Assessment of Osteoarthritis In Vivo.

The abnormal fluctuation of temperature in vivo usually reflects the progression of inflammatory diseases. Noninvasive, real-time, and accurate monitoring and imaging of temperature variation in vivo is advantageous for guiding the early diagnosis and treatment of disease, but it remains difficult to achieve. Herein, we developed a temperature-activated near-infrared-II fluorescence (NIR-II FL) and surface-enhanced Raman scattering (SERS) nanoprobe for long-term monitoring of temperature changes in rat arthritis and timely assessment of the status of osteoarthritis. The thermosensitive polymer bearing NIR-II FL dye was grafted onto the surface of nanoporous core-satellite gold nanostructures to form the nanoprobe, wherein the nanoprobe contains NIR-II FL and Raman reference signals that are independent of temperature change. The ratiometric FL1150/FL1550 and S1528/S2226 values of the nanoprobe exhibited a reversible conversion with temperature changes. The nanoprobe accurately distinguishes the temperature variations in the inflamed joint versus the normal joint in vivo by ratiometric FL and SERS imaging, allowing for an accurate diagnosis of inflammation. Meanwhile, it can continuously monitor fluctuations in temperature over an extended period during the onset and treatment of inflammation. The tested temperature change trend could be used as an indicator for early diagnosis of inflammation and real-time evaluation of therapeutic effects.

Inversion Method for Transformer Winding Hot Spot Temperature Based on Gated Recurrent Unit and Self-Attention and Temperature Lag.

The hot spot temperature of transformer windings is an important indicator for measuring insulation performance, and its accurate inversion is crucial to ensure the timely and accurate fault prediction of transformers. However, existing studies mostly directly input obtained experimental or operational data into networks to construct data-driven models, without considering the lag between temperatures, which may lead to the insufficient accuracy of the inversion model. In this paper, a method for inverting the hot spot temperature of transformer windings based on the SA-GRU model is proposed. Firstly, temperature rise experiments are designed to collect the temperatures of the entire side and top of the transformer tank, top oil temperature, ambient temperature, the cooling inlet and outlet temperatures, and winding hot spot temperature. Secondly, experimental data are integrated, considering the lag of the data, to obtain candidate input feature parameters. Then, a feature selection algorithm based on mutual information (MI) is used to analyze the correlation of the data and construct the optimal feature subset to ensure the maximum information gain. Finally, Self-Attention (SA) is applied to optimize the Gate Recurrent Unit (GRU) network, establishing the GRU-SA model to perceive the potential patterns between output feature parameters and input feature parameters, achieving the precise inversion of the hot spot temperature of the transformer windings. The experimental results show that considering the lag of the data can more accurately invert the hot spot temperature of the windings. The inversion method proposed in this paper can reduce redundant input features, lower the complexity of the model, accurately invert the changing trend of the hot spot temperature, and achieve higher inversion accuracy than other classical models, thereby obtaining better inversion results.

Generation of cutting heat and simulation of core tube wall temperature during coring in primary structure coal seam

When the drilling core method is used to determine the coalbed gas content, the cutting heat generated by the core bit cutting coal will increase the core tube temperature, and the excessively high core tube temperature will have an heating effect on the coal core, which will accelerate the coal core gas desorption rate and increase the gas loss amount. The generation of cutting heat of core bit and the measurement of core tube temperature are the basis for grasping the gas desorption law of coal core and projecting the amount of gas loss. Firstly, the self-developed core tube temperature measurement device was used to conduct on-site core temperature measurement experiments at different cutting speeds. Then, the cutting temperature of core bit was solved by establishing thermodynamic model for cutting coal and heat transfer model of cutting edge. Finally, based on the thermal conductivity characteristics of the core tube, the core tube temperature at different cutting speeds was simulated, and the simulated temperature was compared with the on-site measured temperature to verify the reliability of the model. The results show that when coring in primary structural coal, the temperature change trend of core tube wall temperature measurement point at different cutting speeds is basically consistent, the temperature measurement point at the front end of the core tube mainly goes through a relatively stable period in the drilling process, a sharp rising period in the cutting process, a slow rise and cooling period in the withdrawal process. However, the temperature measurement point at the back end of the core tube wall mainly goes through a relatively stable phase and a slowly increasing phase. The temperature rise of the core bit and the core tube wall are significantly positively correlated with the cutting speed. When coring in hard coal seam and the core depth is not large, the cutting heat generated by the core bit and the coal body is the dominant factor for the temperature rise of the core tube. The core tube wall temperature calculated using the model matches well with the field measured temperature, and the error is small, which fully shows that the coring thermodynamic model is feasible. This study provides a basis for further research on the dynamic distribution characteristic of coal core temperature during coring, which is of profound significance to calculate the gas loss and coalbed gas content.

화물차의 제동장치에서 발생하는 과열 예측방안 연구

Recently, due to the increase in domestic and international online e-commerce platforms and the increase in container traffic at domestic ports, the operating ratio of large trucks has increased, and the number of truck fires is continuously increasing. In particular, spontaneous combustion is the most common cause of truck fires. Various academic approaches have been attempted to prevent truck fires, but due to the lack of research on the spontaneous tire ignition phenomenon that occurs during braking, this research directly designed and manufactured an experimental device to establish an environment similar to the braking system of a truck. A non-contact temperature sensor was installed on the brake device of the experimental device to collect temperature data generated from the brake device. Based on the data collected from the temperature sensor of the brake device and the temperature sensor on the tire surface, the ARIMA model among the time series prediction models was used to Appropriate parameters were selected to suit the temperature change trend, and as a result of comparing and analyzing the measured and predicted data, an accuracy of over 90% was obtained. Based on this, a plan was proposed to reduce the rate of fires in trucks by providing real-time warnings and support for truck drivers to respond to overheating phenomena occurring in the braking system.

Effect of burner structural parameters on combustion characteristics and NOx emission of natural gas

Effect of burner structural parameters on combustion characteristics and NOx emission of natural gas

Prediction of ocean temperature based on ARIMA model

As an important parameter of the Earth's ecosystem, ocean temperature has always been a hot research field for scholars, and the rise in ocean temperature caused by global warming is a topic related to the fate of humanity. The prediction of future ocean temperature is of great significance. The ARIMA model, as a typical prediction model, is highly favored by scholars from various countries due to its accuracy and adaptability. In this paper, the ARIMA multiplication model with seasonal effect is used to forecast the trend of ocean temperature change in the next year, and to prediction graph as well as specific reference values are given. The conclusion is as follows: The global average monthly ocean temperature will continue to rise in the next year. Therefore, this article believes that the government should promptly introduce relevant policies to address the rise in ocean temperature caused by global warming, and people should also fulfill their obligations to protect the environment.