Co/CoO heterojunction rich in oxygen vacancies introduced by O2 plasma embedded in mesoporous walls of carbon nanoboxes covered with carbon nanotubes for rechargeable zinc-air battery

Herein,Co/CoO heterojunction nanoparticles(NPs)rich in oxygen vacancies embedded in mesoporous walls of nitrogen-doped hollow carbon nanoboxes coupled with nitrogen-doped carbon nanotubes(P-Co/CoOV@NHCNB@NCNT)are well designed through zeolite-imidazole framework(ZIF-67)carbonization,chemical vapor deposition,and O_(2) plasma treatment.As a result,the threedimensional NHCNBs coupled with NCNTs and unique heterojunction with rich oxygen vacancies reduce the charge transport resistance and accelerate the catalytic reaction rate of the P-Co/CoOV@NHCNB@NCNT,and they display exceedingly good electrocatalytic performance for oxygen reduction reaction(ORR,halfwave potential[EORR,1/2=0.855 V vs.reversible hydrogen electrode])and oxygen evolution reaction(OER,overpotential(η_(OER,10)=377mV@10mA cm^(−2)),which exceeds that of the commercial Pt/C+RuO_(2) and most of the formerly reported electrocatalysts.Impressively,both the aqueous and flexible foldable all-solid-state rechargeable zinc-air batteries(ZABs)assembled with the P-Co/CoOV@NHCNB@NCNT catalyst reveal a large maximum power density and outstanding long-term cycling stability.First-principles density functional theory calculations show that the formation of heterojunctions and oxygen vacancies enhances conductivity,reduces reaction energy barriers,and accelerates reaction kinetics rates.This work opens up a new avenue for the facile construction of highly active,structurally stable,and cost-effective bifunctional catalysts for ZABs.

Mapping Air Quality Using Remote Sensing Technology: A Case Study of Nairobi County

Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.

Mastering air combat game with deep reinforcement learning

Reinforcement learning has been applied to air combat problems in recent years,and the idea of curriculum learning is often used for reinforcement learning,but traditional curriculum learning suffers from the problem of plasticity loss in neural networks.Plasticity loss is the difficulty of learning new knowledge after the network has converged.To this end,we propose a motivational curriculum learning distributed proximal policy optimization(MCLDPPO)algorithm,through which trained agents can significantly outperform the predictive game tree and mainstream reinforcement learning methods.The motivational curriculum learning is designed to help the agent gradually improve its combat ability by observing the agent's unsatisfactory performance and providing appropriate rewards as a guide.Furthermore,a complete tactical maneuver is encapsulated based on the existing air combat knowledge,and through the flexible use of these maneuvers,some tactics beyond human knowledge can be realized.In addition,we designed an interruption mechanism for the agent to increase the frequency of decisionmaking when the agent faces an emergency.When the number of threats received by the agent changes,the current action is interrupted in order to reacquire observations and make decisions again.Using the interruption mechanism can significantly improve the performance of the agent.To simulate actual air combat better,we use digital twin technology to simulate real air battles and propose a parallel battlefield mechanism that can run multiple simulation environments simultaneously,effectively improving data throughput.The experimental results demonstrate that the agent can fully utilize the situational information to make reasonable decisions and provide tactical adaptation in the air combat,verifying the effectiveness of the algorithmic framework proposed in this paper.

A function-based behavioral modeling method for air combat simulation

Today’s air combat has reached a high level of uncertainty where continuous or discrete variables with crisp values cannot be properly represented using fuzzy sets. With a set of membership functions, fuzzy logic is well-suited to tackle such complex states and actions. However, it is not necessary to fuzzify the variables that have definite discrete semantics.Hence, the aim of this study is to improve the level of model abstraction by proposing multiple levels of cascaded hierarchical structures from the perspective of function, namely, the functional decision tree. This method is developed to represent behavioral modeling of air combat systems, and its metamodel,execution mechanism, and code generation can provide a sound basis for function-based behavioral modeling. As a proof of concept, an air combat simulation is developed to validate this method and the results show that the fighter Alpha built using the proposed framework provides better performance than that using default scripts.

Influences of the Fresh Air Volume on the Removal of Cough-Released Droplets in a Passenger Car of a High-Speed Train Using CFD

The spread and removal of pollution sources,namely,cough-released droplets in three different areas(front,middle,and rear areas)of a fully-loaded passenger car in a high-speed train under different fresh air flow volume were studied using computational fluid dynamics(CFD)method.In addition,the structure of indoor flow fields was also analysed.The results show that the large eddies are more stable and flow faster in the air supply under Mode 2(fresh air volume:2200m3/h)compared to Mode 1(fresh air volume:1100m3/h).By analysing the spreading process of droplets sprayed at different locations in the passenger car and with different particle sizes,the removal trends for droplets are found to be similar under the two air supply modes.However,when increasing the fresh air flow volume,the droplets in the middle and front areas of the passenger car are removed faster.When the droplets had dispersed for 60s,Mode 2 exhibited a removal rate approximately 1%–3%higher than Mode 1 for small and medium-sized droplets with diameters of 10 and 50μm.While those in the rear area,the situation is reversed,with Mode 1 slightly surpassing Mode 2 by 1%–3%.For large droplets with a diameter of 100μm,both modes achieved a removal rate of over 96%in all three regions at the 60 s.The results can provide guidance for air supply modes of passenger cars of high-speed trains,thus suppressing the spread of virus-carrying droplets and reducing the risk of viral infection among passengers.

Air Quality Assessment of Ubeji Community near Petroleum-Related Activities

The escalating global concern over air pollution requires rigorous investigations. This study assesses air quality near residential areas affected by petroleum-related activities in Ubeji Community, utilizing Aeroqual handheld mobile multi-gas monitors and air quality multi-meters. Air sampling occurred on three distinct days using multi-gas monitors and meters, covering parameters such as CO, NO2, CH4, NH3, VOCs, Particulate Matter, Temperature, Relative Humidity, and Air Quality Index. Soil and plant samples were collected and analyzed for physicochemical and organic components. Air pollutant concentrations showed significant fluctuations. Carbon monoxide (CO) ranged from 0.00 to 3.22 ppm, NO2 from 0.00 to 0.10 ppm, CH4 from 4.00 to 2083 ppm, NH3 from 371 to 5086 ppm, and VOCs from 414 to 6135 ppm. Soil analysis revealed low total nitrogen, and undetected BTEX levels. Plant samples displayed a pH range of 7.72 to 9.45. CO concentrations, although below WHO limits, indicated potential vehicular and industrial influences. Fluctuations in NO2 and CH4 were linked to traffic, industrial activities, and gas flaring. NH3 levels suggested diverse pollution sources. The result in this study highlights the dynamic nature of air pollution in Ubeji community, emphasizing the urgent need for effective pollution control measures. Although CO concentrations were within limits, continuous monitoring is essential. Elevated NO2 levels gave information on the impact of industrial activities, while high CH4 concentrations may be associated with gas flaring and illegal refining. The study recommends comprehensive measures and collaborative efforts to address these complex issues, safeguarding both the environment and public health. This study shows the potential synergy between air quality sensors and plants for holistic environmental health assessments, offering valuable insights for environmental assessments and remediation endeavours. The findings call for stringent regulations and collaborative efforts to address air pollution in Ubeji community comprehensively.

Experimental study on secondary air mixing along the bed height in a circulating fluidized bed with a multitracer-gas method

A multitracer-gas method was proposed to study the secondary air(SA)mixing along the bed height in a circulating fluidized bed(CFB)using carbon monoxide(CO),oxygen(O_(2)),and carbon dioxide(CO_(2))as tracer gases.Experiments were carried out on a cold CFB test rig with a cross-section of 0.42 m×0.73 m and a height of 5.50 m.The effects of superficial velocity,SA ratio,bed inventory,and particle diameter on the SA mixing were investigated.The results indicate that there are some differences in the measurement results obtained using different tracer gases,wherein the deviation between CO and CO_(2) ranges from 42%to 66%and that between O_(2) and CO_(2) ranges from 45%to 71%in the lower part of the fluidized bed.However,these differences became less pronounced as the bed height increased.Besides,the high solid concentration and fine particle diameter in the CFB may weaken the difference.The measurement results of different tracer gases show the same trends under the variation of operating parameters.Increasing superficial velocity and SA ratio and decreasing particle diameter result in better mixing of the SA.The effect of bed inventory on SA mixing is not monotonic.

Different bactericidal abilities of plasma-activated saline with various reactive species prepared by surface plasma-activated air and plasma jet combinations

Plasma-activated water(PAW),as an extended form of cold atmospheric-pressure plasma,greatly expands the application of plasma-based technology.The biological effects of PAW are closely related to the aqueous reactive species,which can be regulated by the activation process.In this study,surface plasma-activated air(SAA)and a He+O_(2)plasma jet(Jet)were parallelly combined(the SAA+Jet combination)or sequentially combined(the SAA→Jet combination and the Jet→SAA combination)to prepare plasma-activated saline(PAS).The PAS activated by the combinations exhibited stronger bactericidal effects than that activated by the SAA or the Jet alone.The concentrations of H_(2)O_(2)and NO_(2)^(-)were higher in the PAS activated by the Jet→SAA combination,while ONOO^(-)concentrations were close in the three kinds of PAS and^(1)O_(2)concentrations were higher in the PAS activated by the SAA+Jet combination.The analysis of scavengers also demonstrated that H_(2)O_(2),^(1)O_(2),and ONOO^(-)in the PAS activated by the SAA+Jet combination,and^(1)O_(2)in the PAS activated by the Jet→SAA combination played critical roles in bactericidal effects.Further,the effective placement time of the three PAS varied,and the PAS activated by the Jet→SAA combination could also inactivate 2.6-log_(10)of MRSA cells after placement for more than 60 min.The regulation of reactive species in plasma-activated water via different combinations of plasma devices could improve the directional application of plasma-activated water in the biomedical field.

Effects of dust controls on respirable coal mine dust composition and particle sizes:case studies on auxiliary scrubbers and canopy air curtain

Control of dust in underground coal mines is critical for mitigating both safety and health hazards.For decades,the National Institute of Occupational Safety and Health(NIOSH)has led research to evaluate the effectiveness of various dust control technologies in coal mines.Recent studies have included the evaluation of auxiliary scrubbers to reduce respirable dust downstream of active mining and the use of canopy air curtains(CACs)to reduce respirable dust in key operator positions.While detailed dust characterization was not a focus of such studies,this is a growing area of interest.Using preserved filter samples from three previous NIOSH studies,the current work aims to explore the effect of two different scrubbers(one wet and one dry)and a roof bolter CAC on respirable dust composition and particle size distribution.For this,the preserved filter samples were analyzed by thermogravimetric analysis and/or scanning electron microscopy with energy dispersive X-ray.Results indicate that dust composition was not appreciably affected by either scrubber or the CAC.However,the wet scrubber and CAC appeared to decrease the overall particle size distribution.Such an effect of the dry scrubber was not consistently observed,but this is probably related to the particular sampling location downstream of the scrubber which allowed for significant mixing of the scrubber exhaust and other return air.Aside from the insights gained with respect to the three specific dust control case studies revisited here,this work demonstrates the value of preserved dust samples for follow-up investigation more broadly.

KGTLIR:An Air Target Intention Recognition Model Based on Knowledge Graph and Deep Learning

As a core part of battlefield situational awareness,air target intention recognition plays an important role in modern air operations.Aiming at the problems of insufficient feature extraction and misclassification in intention recognition,this paper designs an air target intention recognition method(KGTLIR)based on Knowledge Graph and Deep Learning.Firstly,the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism.Meanwhile,the accuracy,recall,and F1-score after iteration are introduced to dynamically adjust the sample weights to reduce the probability of misclassification.After that,an intention recognition model based on Knowledge Graph is constructed to predict the probability of the occurrence of different intentions of the target.Finally,the results of the two models are fused by evidence theory to obtain the target’s operational intention.Experiments show that the intention recognition accuracy of the KGTLIRmodel can reach 98.48%,which is not only better than most of the air target intention recognition methods,but also demonstrates better interpretability and trustworthiness.

A Comparison of Airborne Formaldehyde Field Measurements Collected in an Anatomic Pathology Laboratory

Environmental monitoring of airborne formaldehyde (FA) using sensitive methodologies is fundamental to prevent health risks. The objective of this study was to compare three different FA monitoring methods during the daily activities of an anatomic pathology laboratory. Daily eight-hour measurements deriving from Radiello® passive diffusive samplers (PDS), NEMo XT continuous optical sensor (COS), and multi-gas 1512 photoacoustic monitor (MPM) were simultaneously compared over a period of 14 working days. Given the different daily distributions of the measurements performed by the three devices, all measurements were time-aligned for comparison purposes. The 95% limit of agreement (LOA) method was applied to estimate the degree of concordance of each device with respect to the others. Formaldehyde arithmetic mean measured using PDS was 32.6 ± 10.4 ppb (range: 19.8 - 62.7). The simultaneous measures performed by COS and MPM were respectively 42.4 ± 44.8 ppb (range: 7.0 - 175.0) and 189.0 ± 163.7 ppb (range: 40.0 - 2895.4). The MPM geometric mean (171.3 ppb) was approximately five times higher than those derived from COS (32.3 ppb) and PDS (31.4 ppb). The results of the LOA method applied to log-transformed FA data showed the same systematic discrepancies between MPM and the other two devices. A good agreement between PDS and COS could lead to a tailored approach according to the individual specificity of these techniques. This tool may be useful for accurately assessing the risk of FA exposure among healthcare workers. However, the limited specificity of the MPM does not support its use as a monitoring method for FA in the workplace.

Rational Design of Ruddlesden-Popper Perovskite Ferrites as Air Electrode for Highly Active and Durable Reversible Protonic Ceramic Cells

Reversible protonic ceramic cells(RePCCs)hold promise for efficient energy storage,but their practicality is hindered by a lack of high-performance air electrode materials.Ruddlesden-Popper perovskite Sr_(3)Fe_(2)O_(7−δ)(SF)exhibits superior proton uptake and rapid ionic conduction,boosting activity.However,excessive proton uptake during RePCC operation degrades SF’s crystal structure,impacting durability.This study introduces a novel A/B-sites co-substitution strategy for modifying air electrodes,incorporating Sr-deficiency and Nb-substitution to create Sr_(2.8)Fe_(1.8)Nb_(0.2)O_(7−δ)(D-SFN).Nb stabilizes SF’s crystal,curbing excessive phase formation,and Sr-deficiency boosts oxygen vacancy concentration,optimizing oxygen transport.The D-SFN electrode demonstrates outstanding activity and durability,achieving a peak power density of 596 mW cm^(−2)in fuel cell mode and a current density of−1.19 A cm^(−2)in electrolysis mode at 1.3 V,650℃,with excellent cycling durability.This approach holds the potential for advancing robust and efficient air electrodes in RePCCs for renewable energy storage.

Surface air temperature change in the Wuyi Mountains,southeast China

Detecting changes in surface air temperature in mid-and low-altitude mountainous regions is essential for a comprehensive understanding of warming trend with altitude.We use daily surface air temperature data from 64 meteorological stations in Wuyi Mountains and its adjacent regions to analyze the spatio-temporal patterns of temperature change.The results show that Wuyi Mountains have experienced significant warming from 1961 to 2018.The warming trend of the mean temperature is 0.20℃/decade,the maximum temperature is 0.17℃/decade,and the minimum temperature is 0.26℃/decade.In 1961-1990,more than 63%of the stations showed a decreasing trend in annual mean temperature,mainly because the maximum temperature decreased during this period.However,in 1971-2000,1981-2010 and 1991-2018,the maximum,minimum and mean temperatures increased.The fastest increasing trend of mean temperature occurred in the southeastern coastal plains,the quickest increasing trend of maximum temperature occurred in the northwestern mountainous region,and the increase of minimum temperature occurred faster in the southeastern coastal and northwestern mountainous regions than that in the central area.Meanwhile,this study suggests that elevation does not affect warming in the Wuyi Mountains.These results are beneficial for understanding climate change in humid subtropical middle and low mountains.

Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

Maintaining a steady power supply requires accurate forecasting of solar irradiance,since clean energy resources do not provide steady power.The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network(CNN),but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions.This paper proposes a hybrid approach based on deep learning,expanding the feature set by adding new air pollution concentrations,and ranking these features to select and reduce their size to improve efficiency.In order to improve the accuracy of feature selection,a maximum-dependency and minimum-redundancy(mRMR)criterion is applied to the constructed feature space to identify and rank the features.The combination of air pollution data with weather conditions data has enabled the prediction of solar irradiance with a higher accuracy.An evaluation of the proposed approach is conducted in Istanbul over 12 months for 43791 discrete times,with the main purpose of analyzing air data,including particular matter(PM10 and PM25),carbon monoxide(CO),nitric oxide(NOX),nitrogen dioxide(NO_(2)),ozone(O₃),sulfur dioxide(SO_(2))using a CNN,a long short-term memory network(LSTM),and MRMR feature extraction.Compared with the benchmark models with root mean square error(RMSE)results of 76.2,60.3,41.3,32.4,there is a significant improvement with the RMSE result of 5.536.This hybrid model presented here offers high prediction accuracy,a wider feature set,and a novel approach based on air concentrations combined with weather conditions for solar irradiance prediction.

Veno-arterial extracorporeal membrane oxygenation for the treatment of obstructive shock caused by venous air embolism:A case report

BACKGROUND Venous air embolism(VAE)is a potentially lethal condition,with a reported incidence rate of about 0.13%,and the true incidence may be higher since many VAE are asymptomatic.The current treatments for VAE include Durant's maneuver,aspiration and removal of air through venous catheters,and hyperbaric oxygen therapy.For critically ill patients,use of cardiotonic drugs and chest compressions remain useful strategies.The wider availability of extracorporeal membrane oxygenation(ECMO)has brought a new option for VAE patients.CASE SUMMARY A 53-year-old female patient with VAE presented to the emergency clinic due to abdominal pain with fever for 1 d and unconsciousness for 2 h.One day ago,the patient suffered from abdominal pain,fever,and diarrhea.She suddenly became unconscious after going to the toilet during the intravenous infusion of ciprofloxacin 2 h ago,accompanied by nausea and vomiting,during which a small amount of gastric contents were discharged.She was immediately sent to a local hospital,where cranial and chest computed tomography showed bilateral pneumonia as well as accumulated air visible in the right ventricle and pulmonary artery.The condition deteriorated despite endotracheal intubation,rehydration,and other treatments,and the patient was then transferred to our hospital.Veno-arterial ECMO was applied in our hospital,and the patient's condition gradually improved.The patient was successfully weaned from ECMO and extubated after two days.CONCLUSION ECMO may be an important treatment for patients with VAE in critical condition.

Electro-assisted photocatalytic reduction of CO_(2) in ambient air using Ag/TNTAs at the gas-solid interface

The direct conversion of atmospheric CO_(2) into fuel via photocatalysis exhibits significant practical application value in advancing the carbon cycle.In this study,we established an electro-assisted photocatalytic system with dual compartments and interfaces,and coated Ag nanoparticles on the titanium nanotube arrays(TNTAs)by polydopamine modification.In the absence of sacrificial agent and alkali absorption liquid conditions,the stable,efficient and highly selective conversion of CO_(2) to CO at the gas-solid interface in ambient air was realized by photoelectric synergy.Specifically,with the assistance of potential,the CO formation rates reached 194.9μmol h^(−1) m^(−2) and 103.9μmol h^(−1) m^(−2) under ultraviolet and visible light irradiation,respectively;the corresponding CO_(2) conversion rates in ambient air were 30%and 16%,respectively.The excellent catalytic effect is mainly attributed to the formation of P–N heterojunction during the catalytic process and the surface plasmon resonance effect.Additionally,the introduction of solid agar electrolytes effectively inhibits the hydrogen evolution reaction and improves the electron utilization rate.This system promotes the development of photocatalytic technology for practical applications and provides new insights and support for the carbon cycle.

Impact of Air Pollutants on Lung Function and Inflammatory Response in Asthma in Shanghai

Objective Air pollution is a leading public health issue.This study investigated the effect of air quality and pollutants on pulmonary function and inflammation in patients with asthma in Shanghai.Methods The study monitored 27 asthma outpatients for a year,collecting data on weather,patient self-management[daily asthma diary,peak expiratory flow(PEF)monitoring,medication usage],spirometry and serum markers.To explore the potential mechanisms of any effects,asthmatic mice induced by ovalbumin(OVA)were exposed to PM_(2.5).Results Statistical and correlational analyses revealed that air pollutants have both acute and chronic effects on asthma.Acute exposure showed a correlation between PEF and levels of ozone(O_(3))and nitrogen dioxide(NO_(2)).Chronic exposure indicated that interleukin-5(IL-5)and interleukin-13(IL-13)levels correlated with PM_(2.5)and PM_(10)concentrations.In asthmatic mouse models,exposure to PM_(2.5)increased cytokine levels and worsened lung function.Additionally,PM_(2.5)exposure inhibited cell proliferation by blocking the NF-κB and ERK phosphorylation pathways.Conclusion Ambient air pollutants exacerbate asthma by worsening lung function and enhancing Th2-mediated inflammation.Specifically,PM_(2.5)significantly contributes to these adverse effects.Further research is needed to elucidate the mechanisms by which PM_(2.5)impacts asthma.

Spatial Variation in CO_(2) Concentration Improves the Simulated Surface Air Temperature Increase in the Northern Hemisphere

The increasing concentration of atmospheric CO_(2) since the Industrial Revolution has affected surface air temperature.However,the impact of the spatial distribution of atmospheric CO_(2) concentration on surface air temperature biases remains highly unclear.By incorporating the spatial distribution of satellite-derived atmospheric CO_(2) concentration in the Beijing Normal University Earth System Model,this study investigated the increase in surface air temperature since the Industrial Revolution in the Northern Hemisphere(NH) under historical conditions from 1976-2005.In comparison with the increase in surface temperature simulated using a uniform distribution of CO_(2),simulation with a nonuniform distribution of CO_(2)produced better agreement with the Climatic Research Unit(CRU) data in the NH under the historical condition relative to the baseline over the period 1901-30.Hemispheric June-July-August(JJA) surface air temperature increased by 1.28℃ ±0.29℃ in simulations with a uniform distribution of CO_(2),by 1.00℃±0.24℃ in simulations with a non-uniform distribution of CO_(2),and by 0.24℃ in the CRU data.The decrease in downward shortwave radiation in the non-uniform CO_(2) simulation was primarily attributable to reduced warming in Eurasia,combined with feedbacks resulting from increased leaf area index(LAI) and latent heat fluxes.These effects were more pronounced in the non-uniform CO_(2)simulation compared to the uniform CO_(2) simulation.Results indicate that consideration of the spatial distribution of CO_(2)concentration can reduce the overestimated increase in surface air temperature simulated by Earth system models.

Stiffness Characteristics of a Basic Nonlinear Air Spring Model

This study predicts the characteristics of a compressible polytropic air spring model. A second-order nonlinear autonomous air spring model is presented. The proposed model is based on the assumption that polytropic processes occur. Isothermal and isentropic compression and expansion of the air within the spring chambers are the two scenarios that are taken into consideration. In these situations, the air inside the spring chambers compresses and expands, resulting in nonlinear spring restoring forces. The MATLAB/Simulink software environment is used to build a numerical simulation model for the dynamic behavior of the air spring. To quantify the values of the stiffnesses of the proposed models, a numerical solution is run over time for various values of the design parameters. The isentropic process case has a higher dynamic air spring stiffness than the isothermal process case, according to the results. The size of the air spring chamber and the area of the air spring piston influence the air spring stiffness in both situations. It is demonstrated that the stiffness of the air spring increases linearly with increasing piston area and decreases nonlinearly with increasing air chamber length. As long as the ratio of the vibration’s amplitude to the air spring’s chamber length is small, there is good agreement in both scenarios between the linearized model and the full nonlinear model. This implies that linear modeling is a reasonable approximation of the complete nonlinear model in this particular scenario.