Establishment of the thermo-mechanical coupling model of axle box bearings with track irregularity excitation and analysis of its temperature characteristics

As an important component of the running gear of high-speed trains,axle box bearings can cause lubricating grease failure and damage to bearing components under continuous high-temperature operation,which will affect the normal operation of highspeed trains.Therefore,bearing temperature is one of the key parameters to be monitored in the online monitoring system for trains.Based on the thermal network method,this paper establishes a thermal network model for the axle box bearing,considering the radial thermal deformation of the double-row tapered roller bearing components caused by the oil film characteristics and the temperature variations of the lubricating grease.A thermo-mechanical coupling model for the grease-lubricated double-row tapered roller axle box bearing of high-speed trains with track irregularity excitation is established.The correctness of the model is verified using the test bench data,and the temperature of the bearing at different rotational speeds,loads,fault sizes,and ambient temperatures are investigated.

Predictive modeling of critical temperatures in magnesium compounds using transfer learning

This study presents a transfer learning approach for discovering potential Mg-based superconductors utilizing a comprehensive target dataset.Initially,a large source dataset(Bandgap dataset)comprising approximately∼75k compounds is utilized for pretraining,followed by fine-tuning with a smaller Critical Temperature(T_(c))dataset containing∼300 compounds.Comparatively,there is a significant improvement in the performance of the transfer learning model over the traditional deep learning(DL)model in predicting Tc.Subsequently,the transfer learning model is applied to predict the properties of approximately 150k compounds.Predictions are validated computationally using density functional theory(DFT)calculations based on lattice dynamics-related theory.Moreover,to demonstrate the extended predictive capability of the transfer learning model for new materials,a pool of virtual compounds derived from prototype crystal structures from the Materials Project(MP)database is generated.T_(c) predictions are obtained for∼3600 virtual compounds,which underwent screening for electroneutrality and thermodynamic stability.An Extra Trees-based model is trained to utilize E_(hull)values to obtain thermodynamically stable materials,employing a dataset containing Ehull values for approximately 150k materials for training.Materials with Ehull values exceeding 5 meV/atom were filtered out,resulting in a refined list of potential Mg-based superconductors.This study showcases the effectiveness of transfer learning in predicting superconducting properties and highlights its potential for accelerating the discovery of Mg-based materials in the field of superconductivity.

Gyro-Landau-fluid simulations of impurity effects on ion temperature gradient driven turbulence transport

The effects of impurities on ion temperature gradient(ITG)driven turbulence transport in tokamak core plasmas are investigated numerically via global simulations of microturbulence with carbon impurities and adiabatic electrons.The simulations use an extended fluid code(ExFC)based on a four-field gyro-Landau-fluid(GLF)model.The multispecies form of the normalized GLF equations is presented,which guarantees the self-consistent evolution of both bulk ions and impurities.With parametric profiles of the cyclone base case,well-benchmarked ExFC is employed to perform simulations focusing on different impurity density profiles.For a fixed temperature profile,it is found that the turbulent heat diffusivity of bulk ions in a quasi-steady state is usually lower than that without impurities,which is contrary to the linear and quasilinear predictions.The evolutions of the temperature gradient and heat diffusivity exhibit a fast relaxation process,indicating that the destabilization of the outwardly peaked impurity profile is a transient state response.Furthermore,the impurity effects from different profiles can obviously influence the nonlinear critical temperature gradient,which is likely to be dominated by linear effects.These results suggest that the improvement in plasma confinement could be attributed to the impurities,most likely through adjusting both heat diffusivity and the critical temperature gradient.

An Explanation of the Temperature-Dependent Upper Critical Field Data of H3S on the Basis of the Thermodynamics of a Superconductor in a Magnetic Field

Excellent fits to a couple of the data-sets on the temperature (T)-dependent upper critical field (Hc2) of H3S (critical temperature, Tc ≈ 200 K at pressure ≈ 150 GPa) reported by Mozaffari, et al. (2019) were obtained by Talantsev (2019) in an approach based on an ingenious mix of the Ginzberg-Landau (GL), the Werthamer, Helfand and Hohenberg (WHH), and the Gor’kov, etc., theories which have individually been employed for the same purpose for a long time. Up to the lowest temperature (TL) in each of these data-sets, similarly accurate fits have also been obtained by Malik and Varma (2023) in a radically different approach based on the Bethe-Salpeter equation (BSE) supplemented by the Matsubara and the Landau quantization prescriptions. For T TL, however, while the (GL, WHH, etc.)-based approach leads to Hc2(0) ≈ 100 T, the BSE-based approach leads to about twice this value even at 1 K. In this paper, a fit to one of the said data-sets is obtained for the first time via a thermodynamic approach which, up to TL, is as good as those obtained via the earlier approaches. While this is interesting per se, another significant result of this paper is that for T TL it corroborates the result of the BSE-based approach.

Regularity analysis of resilient modulus for hot-mix asphalt with large temperature fluctuations

To evaluate the regularity of resilient modulus for hot-mix asphalt(HMA) under large temperature fluctuations,back propagation(BP) neural network technology was used to analyze the continuous change of HMA resilient modulus.Firstly,based on the abundant data,the training model of HMA resilient modulus was established by using BP neural network technology.Subsequently,BP neural network prediction and regression analysis were performed,and the prediction model of HMA resilient modulus at different temperatures(-50℃ to 60℃) was obtained,which fully considered multi-factor and nonlinearity.Finally,the fitted theoretical model can be used to evaluate the HMA performance under the condition of large temperature fluctuations,and the rationality of theoretical model was verified by taking Harbin region as an example.It was found that the relationship between HMA resilient modulus and temperatures can be described by inverse tangent function.And the key parameters of theoretical model can be used to evaluate the continuous change characteristics of HMA resilient modulus with large temperature fluctuations.The results can further improve the HMA performance evaluation system and have certain theoretical value.

Fracture characteristics and criteria of Hastelloy C-276 alloy during high-temperature deformation

Hot tensile tests were performed on Hastelloy C-276 alloy in the temperature range of 850−1150℃ and strain rate range of 0.01−10 s^(−1) to reveal its fracture characteristics and critical fracture failure conditions during high temperature deformation process.Short-term aging treatments were also conducted to analyze the effects of precipitation on the fracture behaviors in conjunction with the experimental results obtained from the hot tensile tests.It was observed that the main precipitates in Hastelloy C-276 alloy under hot tensile deformation and short-term aging treatment were identified as M_(6)C carbides,around which the microscopic voids nucleate when the external forces were applied.Considering the effects of deformation temperature and strain rate,two failure criteria based on Zener-Hollomon parameter were developed to describe the fracture behaviors of Hastelloy C-276 alloy deforming at elevated temperatures.Finite element method(FEM)coupling with the proposed failure criteria was used to examine the validity by comparing the predicted values with the experimental data,and the comparison results indicate that the established failure criteria were capable of predicting the fracture behaviors of Hastelloy C-276 alloy in hot deformation process.

On the Role of Chemical Potential in Determining the Temperature Dependent Critical Magnetic Field and the Penetration Depth of Superconductors

Dealing with both elemental and high-Tc superconductors (SCs) - Sn, Nb and Pb belonging to the former category, and MgB2 and different samples of YBCO to the latter - we show that the difference in the values of their critical magnetic field Hc1,c2 and the penetration depth λL(0) is, remarkably, attributable predominantly to the difference in the values of a single parameter, viz., the chemical potential (μ) close to their critical temperatures (Tcs). Based directly on the dynamics of pairing in a magnetic field and the corresponding number equation, our approach relates Hc1,c2 of an SC with the following set of its properties: S1 = {μ, Tc, Debye temperature, effective mass of the electron, magnetic interaction parameter, Landau index}. Hence, it provides an alternative to the approach followed by Talantsev [Mod. Phys. Lett. B 33, 1950195 (2019)] who has shown by ingeniously combining the results of various well-established theories that Hc2 of an SC can be calculated via four different equations, each of which invokes two or more properties from its sample-specific set S2 = {Tc, gap, coherence length, λL(0), jump in sp. ht.}, which is radically different from S1.

Urban Surroundings Influence on Air Temperature in a Small Urban Area of Curitiba-Brazil

The formation of urban climates constitutes a distinctive system intrinsically linked to the urban environment. This study aims to delve into the impact of the urban environment on climatic variables. The Urban Weather Generator (UWG) algorithm was employed to generate climatic data, facilitating the creation of an epw climate file that corresponds to the urban characteristics surrounding the Centro Politécnico campus at the Federal University of Paraná (UFPR). Comprehensive analyses encompassing land use, occupancy patterns, albedo, surface absorption, anthropogenic heat, and architectural attributes were conducted. A comparative assessment between the UWG-derived air temperature values and meteorological station data revealed that the UWG effectively characterizes the air temperature patterns around the UFPR campus. The anticipated air temperature values consistently surpass the original dataset (SWERA), which was utilized as input, primarily during the hours from 3 p.m. to 7 a.m., showcasing the unmistakable urban heat island phenomenon.

Quality of Service and Security on Cisco Network Devices, Coupled with the Development of a Mobile Application Prototype Software for Server Room Temperature Monitoring

In an era where digital technology is paramount, higher education institutions like the University of Zambia (UNZA) are employing advanced computer networks to enhance their operational capacity and offer cutting-edge services to their academic fraternity. Spanning across the Great East Road campus, UNZA has established one of the most extensive computer networks in Zambia, serving a burgeoning community of over 20,000 active users through a Metropolitan Area Network (MAN). However, as the digital landscape continues to evolve, it is besieged with burgeoning challenges that threaten the very fabric of network integrity—cyber security threats and the imperatives of maintaining high Quality of Service (QoS). In an effort to mitigate these threats and ensure network efficiency, the development of a mobile application to monitor temperatures in the server room was imperative. According to L. Wei, X. Zeng, and T. Shen, the use of wireless sensory networks to monitor the temperature of train switchgear contact points represents a cost-effective solution. The system is based on wireless communication technology and is detailed in their paper, “A wireless solution for train switchgear contact temperature monitoring and alarming system based on wireless communication technology”, published in the International Journal of Communications, Network and System Sciences, vol. 8, no. 4, pp. 79-87, 2015 [1]. Therefore, in this study, a mobile application technology was explored for monitoring of temperatures in the server room in order to aid Cisco device performance. Additionally, this paper also explores the hardening of Cisco device security and QoS which are the cornerstones of this study.

Association between Low Ambient Temperature and the Severity of Acute Ischemic Stroke

Objective:To explore the relationships among ambient temperature,ischemic stroke severity,and blood pressure.Methods:Meteorological data(2005–2015)were collected from the Guangzhou Meteorological Data Service.Ischemic stroke patients from the Department of Neurology of the First Affiliated Hospital,Sun Yat-sen University were retrospectively evaluated,each winter from 2005 to 2015.Patient demographics,baseline measurements,and National Institute of Health Stroke Scale(NIHSS)score were evaluated.Results:Three hundred sixty-two patients were included.The median latency from symptom onset to admission was 2 d(IQR:1–3 d).During recruitment,the highest and lowest temperatures were 39℃and 1.3℃,respectively.Hypertension was the most common comorbidity(75.1%).NIHSS scores at admission and discharge were higher in the cold-exposed group than in the controls regardless of the average temperature at admission.In addition,systolic and diastolic blood pressure values at admission were higher in the cold-exposed group than in the controls.When stratified by hypertensive status,the average and minimum temperatures at admission were negatively associated with systolic and diastolic blood pressure values in hypertensive patients.Reductions in the average and minimum temperatures at symptom onset were associated with more severe stroke.Conclusion:Ischemic stroke patients with symptom onset in winter had higher systolic blood pressure values and more serious neurologic deficits upon admission.

Effects of the extrusion parameters on microstructure,texture and room temperature mechanical properties of extruded Mg-2.49Nd-1.82Gd-0.2Zn-0.2Zr alloy

Microstructure,texture,and mechanical properties of the extruded Mg-2.49Nd-1.82Gd-0.2Zn-0.2Zr alloy were investigated at different extrusion temperatures(260 and 320℃),extrusion ratios(10:1,15:1,and 30:1),and extrusion speeds(3 and 6 mm/s).The experimental results exhibited that the grain sizes after extrusion were much finer than that of the homogenized alloy,and the second phase showed streamline distribution along the extrusion direction(ED).With extrusion temperature increased from 260 to 320℃,the microstructure,texture,and mechanical properties of alloys changed slightly.The dynamic recrystallization(DRX)degree and grain sizes enhanced as the extrusion ratio increased from 10:1 to 30:1,and the strength gradually decreased but elongation(EL)increased.With the extrusion speed increased from 3 to 6 mm/s,the grain sizes and DRX degree increased significantly,and the samples presented the typical<2111>-<1123>rare-earth(RE)textures.The alloy extruded at 260℃ with extrusion ratio of 10:1 and extrusion speed of 3 mm/s showed the tensile yield strength(TYS)of 213 MPa and EL of 30.6%.After quantitatively analyzing the contribution of strengthening mechanisms,it was found that the grain boundary strengthening and dislocation strengthening played major roles among strengthening contributions.These results provide some guidelines for enlarging the industrial application of extruded Mg-RE alloy.

Algorithms for Empirical Equations in Terms of the Cosmic Microwave Background Temperature

Previously, we presented several empirical equations using the cosmic microwave background (CMB) temperature. Next, we propose an empirical equation for the fine-structure constant. Considering the compatibility among these empirical equations, the CMB temperature (Tc) and gravitational constant (G) were calculated to be 2.726312 K and 6.673778 × 10−11 m3∙kg−1∙s−2, respectively. Every equation can be explained numerically in terms of the Compton length of an electron (λe), the Compton length of a proton (λp) and α. Furthermore, every equation can also be explained in terms of the Avogadro number and the number of electrons at 1 C. We show that every equation can be described in terms of the Planck constant. Then, the ratio of the gravitational force to the electric force can be uniquely determined with the assumption of minimum mass. In this report, we describe the algorithms used to explain these equations in detail. Thus, there are no dimension mismatch problems.

Ideal Bi‑Based Hybrid Anode Material for Ultrafast Charging of Sodium‑Ion Batteries at Extremely Low Temperatures

Sodium-ion batteries have emerged as competitive substitutes for low-temperature applications due to severe capacity loss and safety concerns of lithium-ion batteries at−20°C or lower.However,the key capability of ultrafast charging at ultralow temperature for SIBs is rarely reported.Herein,a hybrid of Bi nanoparticles embedded in carbon nanorods is demonstrated as an ideal material to address this issue,which is synthesized via a high temperature shock method.Such a hybrid shows an unprecedented rate performance(237.9 mAh g^(−1) at 2 A g^(−1))at−60℃,outperforming all reported SIB anode materials.Coupled with a Na_(3)V_(2)(PO_(4))_(3)cathode,the energy density of the full cell can reach to 181.9 Wh kg^(−1) at−40°C.Based on this work,a novel strategy of high-rate activation is proposed to enhance performances of Bi-based materials in cryogenic conditions by creating new active sites for interfacial reaction under large current.

Furnace Temperature Curve Optimization Model Based on Differential Evolution Algorithm

When soldering electronic components onto circuit boards,the temperature curves of the reflow ovens across different zones and the conveyor belt speed significantly influence the product quality.This study focuses on optimizing the furnace temperature curve under varying settings of reflow oven zone temperatures and conveyor belt speeds.To address this,the research sequentially develops a heat transfer model for reflow soldering,an optimization model for reflow furnace conditions using the differential evolution algorithm,and an evaluation and decision model combining the differential evolution algorithm with the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)method.This approach aims to determine the optimal furnace temperature curve,zone temperatures of the reflow oven,and the conveyor belt speed.

Novel Methodologies for Preventing Crack Propagation in Steel Gas Pipelines Considering the Temperature Effect

Using the software ANSYS-19.2/Explicit Dynamics,this study performedfinite-element modeling of the large-diameter steel pipeline cross-section for the Beineu-Bozoy-Shymkent gas pipeline with a non-through straight crack,strengthened by steel wire wrapping.The effects of the thread tensile force of the steel winding in the form of single rings at the crack edges and the wires with different winding diameters and pitches were also studied.The results showed that the strengthening was preferably executed at a minimum value of the thread tensile force,which was 6.4%more effective than that at its maximum value.The analysis of the influence of the winding dia-meters showed that the equivalent stresses increased by 32%from the beginning of the crack growth until the wire broke.The increment in winding diameter decelerated the disclosure of the edge crack and reduced its length by 8.2%.The analysis of the influence of the winding pitch showed that decreasing the distance between the winding turns also led to a 33.6%reduction in the length of the straight crack and a 7.9%reduction in the maximum stres-ses on the strengthened pipeline cross-section.The analysis of the temperature effect on the pipeline material,within a range from-40℃ to+50℃,resulted in a crack length change of up to 5.8%.As the temperature dropped,the crack length decreased.Within such a temperature range,the maximum stresses were observed along the cen-tral area of the crack,which were equal to 413 MPa at+50℃ and 440 MPa at-40℃.The results also showed that the presence of the steel winding in the pipeline significantly reduced the length of crack propagation up to 8.4 times,depending on the temperature effect and design parameters of prestressing.This work integrated the existing methods for crack localization along steel gas pipelines.

A PHYB-PIF4-auxin module promotes tomato graft formation in response to elevated ambient temperature

Grafting is an effective technique for increasing the resistance of vegetables to biotic and abiotic stresses.It has been widely applied to produce solanaceous and melon vegetables.Temperature is an important external factor affecting graft formation.However,the molecular mechanism by which external ambient temperature affects tomato graft formation remains unclear.In this study,we demonstrated that elevating ambient temperature during grafting to 35℃ for more than 24 h after grafting accelerated vascular reconnection.We generated self-or heterografted combinations between phyB1B2 and pif4 loss-of-function mutant and wild-type plants,and were mutants unresponsive to graft formation at elevated ambient temperature.In addition,elevated ambient temperature induced SlPIF4 expression during grafting.SlPIF4 directly binds the promoters of auxin biosynthesis genes SlYUCCAs and activates their expression.Further investigation revealed auxin accumulation in the graft junction under elevated ambient temperature.The results illuminate the mechanism by which the PHYB-PIF4-auxin module promotes tomato graft formation in response to elevated ambient temperature.

Subsurface Temperature and Salinity Structures Inversion Using a Stacking-Based Fusion Model from Satellite Observations in the South China Sea

Three-dimensional ocean subsurface temperature and salinity structures(OST/OSS)in the South China Sea(SCS)play crucial roles in oceanic climate research and disaster mitigation.Traditionally,real-time OST and OSS are mainly obtained through in-situ ocean observations and simulation by ocean circulation models,which are usually challenging and costly.Recently,dynamical,statistical,or machine learning models have been proposed to invert the OST/OSS from sea surface information;however,these models mainly focused on the inversion of monthly OST and OSS.To address this issue,we apply clustering algorithms and employ a stacking strategy to ensemble three models(XGBoost,Random Forest,and LightGBM)to invert the real-time OST/OSS based on satellite-derived data and the Argo dataset.Subsequently,a fusion of temperature and salinity is employed to reconstruct OST and OSS.In the validation dataset,the depth-averaged Correlation(Corr)of the estimated OST(OSS)is 0.919(0.83),and the average Root-Mean-Square Error(RMSE)is0.639°C(0.087 psu),with a depth-averaged coefficient of determination(R~2)of 0.84(0.68).Notably,at the thermocline where the base models exhibit their maximum error,the stacking-based fusion model exhibited significant performance enhancement,with a maximum enhancement in OST and OSS inversion exceeding 10%.We further found that the estimated OST and OSS exhibit good agreement with the HYbrid Coordinate Ocean Model(HYCOM)data and BOA_Argo dataset during the passage of a mesoscale eddy.This study shows that the proposed model can effectively invert the real-time OST and OSS,potentially enhancing the understanding of multi-scale oceanic processes in the SCS.

Advancements in implantable temperature sensors:Materials,mechanisms,and biological applications

Implantable temperature sensors are revolutionizing physiological monitoring and playing a crucial role in diagnostics,therapeutics,and life sciences research.This review classifies the materials used in these sensors into three categories:metal-based,inorganic semiconductor,and organic semiconductor materials.Metal-based materials are widely used in medical and industrial applications due to their linearity,stability,and reliability.Inorganic semiconductors provide rapid response times and high miniaturization potential,making them promising for biomedical and environmental monitoring.Organic semiconductors offer high sensitivity and ease of processing,enabling the development of flexible and stretchable sensors.This review analyzes recent studies for each material type,covering design principles,performance characteristics,and applications,highlighting key advantages and challenges regarding miniaturization,sensitivity,response time,and biocompatibility.Furthermore,critical performance parameters of implantable temperature sensors based on different material types are summarized,providing valuable references for future sensor design and optimization.The future development of implantable temperature sensors is discussed,focusing on improving biocompatibility,long-term stability,and multifunctional integration.These advancements are expected to expand the application potential of implantable sensors in telemedicine and dynamic physiological monitoring.

An integrated physiology and proteomics analysis reveals the response of wheat grain to low temperature stress during booting

Low temperature(LT)in spring has become one of the principal abiotic stresses that restrict the growth and development of wheat.Diverse analyses were performed to investigate the mechanism underlying the response of wheat grain development to LT stress during booting.These included morphological observation,measurements of starch synthase activity,and determination of amylose and amylopectin content of wheat grain after exposure to treatment with LT during booting.Additionally,proteomic analysis was performed using tandem mass tags(TMT).Results showed that the plumpness of wheat grains decreased after LT stress.Moreover,the activities of sucrose synthase(SuS,EC 2.4.1.13)and ADP-glucose pyrophosphorylase(AGPase,EC 2.7.7.27)exhibited a significant reduction,leading to a significant reduction in the contents of amylose and amylopectin.A total of 509 differentially expressed proteins(DEPs)were identified by proteomics analysis.The Gene Ontology(GO)enrichment analysis showed that the protein difference multiple in the nutritional repository activity was the largest among the molecular functions,and the up-regulated seed storage protein(ssP)played an active role in the response of grains to LT stress and subsequent damage.The Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis showed that LT stress reduced the expression of DEPs such as sucrose phosphate synthase(SPS),glucose-1-phosphate adenylyltransferase(glgC),andβ-fructofuranosidase(FFase)in sucrose and starch metabolic pathways,thus affecting the synthesis of grain starch.In addition,many heat shock proteins(HsPs)were found in the protein processing in endoplasmic reticulum pathways,which can resist some damage caused by LT stress.These findings provide a new theoretical foundation for elucidating the underlying mechanism governing wheat yield developmentafterexposuretoLTstress inspring.

Evaluation of ERA5 reanalysis temperature data over the Qilian Mountains of China

Air temperature is an important indicator to analyze climate change in mountainous areas.ERA5 reanalysis air temperature data are important products that were widely used to analyze temperature change in mountainous areas.However,the reliability of ERA5 reanalysis air temperature over the Qilian Mountains(QLM)is unclear.In this study,we evaluated the reliability of ERA5 monthly averaged reanalysis 2 m air temperature data using the observations at 17 meteorological stations in the QLM from 1979 to 2017.The results showed that:ERA5 reanalysis monthly averaged air temperature data have a good applicability in the QLM in general(R2=0.99).ERA5 reanalysis temperature data overestimated the observed temperature in the QLM in general.Root mean square error(RMSE)increases with the increasing of elevation range,showing that the reliability of ERA5 reanalysis temperature data is worse in higher elevation than that in lower altitude.ERA5 reanalysis temperature can capture observational warming rates well.All the smallest warming rates of observational temperature and ERA5 reanalysis temperature are found in winter,with the warming rates of 0.393°C/10a and 0.360°C/10a,respectively.This study will provide a reference for the application of ERA5 reanalysis monthly averaged air temperature data at different elevation ranges in the Qilian Mountains.