A comparison study on structure-function relationship of polysaccharides obtained from sea buckthorn berries using different methods:antioxidant and bile acid-binding capacity

In this study,the structural characters,antioxidant activities and bile acid-binding ability of sea buckthorn polysaccharides(HRPs)obtained by the commonly used hot water(HRP-W),pressurized hot water(HRP-H),ultrasonic(HRP-U),acid(HRP-C)and alkali(HRP-A)assisted extraction methods were investigated.The results demonstrated that extraction methods had significant effects on extraction yield,monosaccharide composition,molecular weight,particle size,triple-helical structure,and surface morphology of HRPs except for the major linkage bands.Thermogravimetric analysis showed that HRP-U with filamentous reticular microstructure exhibited better thermal stability.The HRP-A with the lowest molecular weight and highest arabinose content possessed the best antioxidant activities.Moreover,the rheological analysis indicated that HRPs with higher galacturonic acid content and molecular weight showed higher viscosity and stronger crosslinking network(HRP-C,HRP-W and HRP-U),which exhibited stronger bile acid binding capacity.The present findings provide scientific evidence in the preparation technology of sea buckthorn polysaccharides with good antioxidant and bile acid binding capacity which are related to the structure affected by the extraction methods.

A coupled thermo-mechanical peridynamic model for fracture behavior of granite subjected to heating and water-cooling processes

Thermal damage and thermal fracture of rocks are two important indicators in geothermal mining projects.This paper investigates the effects of heating and water-cooling on granite specimens at various temperatures.The laboratory uniaxial compression experiments were also conducted.Then,a coupled thermo-mechanical ordinary state-based peridynamic(OSB-PD)model and corresponding numerical scheme were developed to simulate the damage of rocks after the heating and cooling processes,and the change of crack evolution process was predicted.The results demonstrate that elevated heating temperatures exacerbate the thermal damage to the specimens,resulting in a decrease in peak strength and an increase in ductility of granite.The escalating occurrence of thermal-induced cracks significantly affects the crack evolution process during the loading phase.The numerical results accurately reproduce the damage and fracture characteristics of the granite under different final heating temperatures(FHTs),which are consistent with the test results in terms of strength,crack evolution process,and failure mode.

Low-energy-consumption temperature swing system for CO_(2) capture by combining passive radiative cooling and solar heating

Temperature-swing adsorption(TSA)is an effective technique for CO_(2) capture,but the temperature swing procedure is energy-intensive.Herein,we report a low-energy-consumption system by combining passive radiative cooling and solar heating for the uptake of CO_(2) on commercial activated carbons(CACs).During adsorption,the adsorbents are coated with a layer of hierarchically porous poly(vinylidene fluoride-co-hexafluoropropene)[P(VdF-HFP)HP],which cools the adsorbents to a low temperature under sunlight through radiative cooling.For desorption,CACs with broad absorption of the solar spectrum are exposed to light irradiation for heating.The heating and cooling processes are completely driven by solar energy.Adsorption tests under mimicked sunlight using the CACs show that the performance of this system is comparable to that of the traditional ones.Furthermore,under real sunlight irradiation,the adsorption capacity of the CACs can be well maintained after multiple cycles.The present work may inspire the development of new temperature swing procedures with little energy consumption.

Insight into the capacity degradation and structural evolution of single-crystal Ni-rich cathodes

Single-crystal Ni-rich cathodes are a promising candidate for high-energy lithium-ion batteries due to their higher structural and cycling stability than polycrystalline materials.However,the phase evolution and capacity degradation of these single-crystal cathodes during continuous lithation/delithation cycling remains unclear.Understanding the mapping relationship between the macroscopic electrochemical properties and the material physicochemical properties is crucial.Here,we investigate the correlation between the physical-chemical characteristics,phase transition,and capacity decay using capacity differential curve feature identification and in-situ X-ray spectroscopic imaging.We systematically clarify the dominant mechanism of phase evolution in aging cycling.Appropriately high cut-off voltages can mitigate the slow kinetic and electrochemical properties of single-crystal cathodes.We also find that second-order differential capacity discharge characteristic curves can be used to identify the crystal structure disorder of Ni-rich cathodes.These findings constitute a step forward in elucidating the correlation between the electrochemical extrinsic properties and the physicochemical intrinsic properties and provide new perspectives for failure analysis of layered electrode materials.

Effect of Trimetazidine on Functional Capacity in Patient with Ischaemic Cardiomyopathy (TOFCAPI)

Objective: This study aimed to evaluate the efficacy of trimetazidine on exercise capacity via a six-minute walk test in patients with ischaemic cardiomyopathy and also evaluate the effect of trimetazidine on left ventricular function via echocardiography in the same population. Methods: This prospective observational study, conducted at the National Institute of Cardiovascular Diseases in Dhaka, Bangladesh, enrolled 200 patients with ischaemic cardiomyopathy and a depressed left ventricular ejection fraction (LVEF Results: In this study (n = 200) of ischaemic cardiomyopathy patients, the mean age was 58 years, with 76% of the patients being male. All study subjects received GDMT (Guideline-Directed Medical Therapy) for angina and heart failure. Those who received the modified released form of trimetazidine developed lesions during the 1st and 2nd follow-ups, during which the LVEF, LVIDd, and six-minute walk distance significantly improved (p Conclusion: The findings of the present study demonstrated that the addition of modified-release trimetazidine to GDMT can improve exercise capacity and left ventricular function in patients with ischaemic cardiomyopathy.

Functional Capacity of Patients with Venous Thromboembolic Disease Six to Twelve Months after Treatment

Background: Venous thromboembolism (VTE) is among the leading causes of hospital-related disability-adjusted life years lost. We aimed to determine the prevalence and determinants of functional capacity impairment six to twelve months after an acute VTE event. Methods: This was a cross-sectional study conducted between January and April 2021 in two referral hospitals of Yaoundé, including consenting adult patients admitted to these hospitals six to twelve months ago for VTE. We excluded dead patients and those with any comorbidity or symptoms limiting physical activity. The functional outcome was assessed with the six-minute walk test. Functional capacity impairment was defined as walking distance lower than the expected value. Results: We included 27 cases in this study with a mean age of 53.2 ± 14.4 years. The prevalence of functional capacity impairment was 29.6% (95% CI: 14.8 - 48.1). Factors associated with poor functional outcome were obesity (OR: 59.5;95% CI: 4.6 - 767.2;p - 207.4;p = 0.017), massive PE (OR: 30;95% CI: 2.5 - 354;p = 0.004), and poor adherence to treatment (OR: 30.3;95% CI: 2.5 - 333.3;p = 0.004). Conclusion: Functional capacity impairment is common in the medium-term after VTE and factors associated with this poor outcome are obesity, the severity of the VTE, and poor adherence to treatment.

Effect of speed humps on instantaneous traffic emissions in a microscopic model with limited deceleration capacity

As a common transportation facility, speed humps can control the speed of vehicles on special road sections to reduce traffic risks. At the same time, they also cause instantaneous traffic emissions. Based on the classic instantaneous traffic emission model and the limited deceleration capacity microscopic traffic flow model with slow-to-start rules, this paper has investigated the impact of speed humps on traffic flow and the instantaneous emissions of vehicle pollutants in a single lane situation. The numerical simulation results have shown that speed humps have significant effects on traffic flow and traffic emissions. In a free-flow region, the increase of speed humps leads to the continuous rise of CO_(2), NO_(X) and PM emissions. Within some density ranges, one finds that these pollutant emissions can evolve into some higher values under some random seeds. Under other random seeds, they can evolve into some lower values. In a wide moving jam region, the emission values of these pollutants sometimes appear as continuous or intermittent phenomenon. Compared to the refined Na Sch model, the present model has lower instantaneous emissions such as CO_(2), NO_(X) and PM and higher volatile organic components(VOC) emissions. Compared to the limited deceleration capacity model without slow-to-start rules, the present model also has lower instantaneous emissions such as CO_(2), NO_(X) and PM and higher VOC emissions in a wide moving jam region. These results can also be confirmed or explained by the statistical values of vehicle velocity and acceleration.

Heating of nanoparticles and their environment by laser radiation and applications

This review considers the fundamental dynamic processes involved in the laser heating of metal nanoparticles and their subsequent cooling.Of particular interest are the absorption of laser energy by nanoparticles,the heating of a single nanoparticle or an ensemble thereof,and the dissipation of the energy of nanoparticles due to heat exchange with the environment.The goal is to consider the dependences and values of the temperatures of the nanoparticles and the environment,their time scales,and other parameters that describe these processes.Experimental results and analytical studies on the heating of single metal nanoparticles by laser pulses are discussed,including the laser thresholds for initiating subsequent photothermal processes,how temperature influences the optical properties,and the heating of gold nanoparticles by laser pulses.Experimental studies of the heating of an ensemble of nanoparticles and the results of an analytical study of the heating of an ensemble of nanoparticles and the environment by laser radiation are considered.Nanothermometry methods for nanoparticles under laser heating are considered,including changes in the refractive indices of metals and spectral thermometry of optical scattering of nanoparticles,Raman spectroscopy,the thermal distortion of the refractive index of an environment heated by a nanoparticle,and thermochemical phase transitions in lipid bilayers surrounding a heated nanoparticle.Understanding the sequence of events after radiation absorption and their time scales underlies many applications of nanoparticles.The applicationfields for the laser heating of nanoparticles are reviewed,including thermochemical reactions and selective nanophotothermolysis initiated in the environment by laser-heated nanoparticles,thermal radiation emission by nanoparticles and laser-induced incandescence,electron and ion emission of heated nanoparticles,and optothermal chemical catalysis.Applications of the laser heating of nanoparticles in laser nanomedicine are of particular interest.Significant emphasis is given to the proposed analytical approaches to modeling and calculating the heating processes under the action of a laser pulse on metal nanoparticles,taking into account the temperature dependences of the parameters.The proposed models can be used to estimate the parameters of lasers and nanoparticles in the various applicationfields for the laser heating of nanoparticles.

Research on the Method of Heat Preservation and Heating for the Drilling System of Polar Offshore Drilling Platform

This study investigates the heat dissipation mechanism of the insulation layer and other plane insulation layers in the polar drilling rig system.Combining the basic theory of heat transfer with the environmental requirements of polar drilling operations and the characteristics of polar drilling processes,we analyze the factors that affect the insulation effect of the drilling rig system.These factors include the thermal conductivity of the insulation material,the thickness of the insulation layer,ambient temperature,and wind speed.We optimize the thermal insulation material of the polar drilling rig system using a steady-state method to measure solid thermal conductivity.By analyzing the distribution of temperature in space after heating,we optimize the distribution and air outlet angle of the heater using Fluent hydrodynamics software.The results demonstrate that under polar conditions,polyisocyanurate with stable thermodynamic properties is selected as the thermal insulation material.The selection of thermal insulation material and thickness significantly affects the thermal insulation effect of the system but has little effect on its heating effect.Moreover,when the air outlet angle of the heater is set to 32.5°,the heating efficiency of the system can be effectively improved.According to heat transfer equations and heat balance theory,we determine that the heating power required for the system to reach 5°C is close to numerical simulation.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.

Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

Durable K-ion batteries with 100% capacity retention up to 40,000 cycles

Currently,the major challenge in terms of research on K-ion batteries is to ensure that they possess satisfactory cycle stability and specific capacity,especially in terms of the intrinsically sluggish kinetics induced by the large radius of K+ions.Here,we explore high-performance K-ion half/full batteries with high rate capability,high specific capacity,and extremely durable cycle stability based on carbon nanosheets with tailored N dopants,which can alleviate the change of volume,increase electronic conductivity,and enhance the K+ion adsorption.The as-assembled K-ion half-batteries show an excellent rate capability of 468 mA h g^(−1) at 100 mA g^(−1),which is superior to those of most carbon materials reported to date.Moreover,the as-assembled half-cells have an outstanding life span,running 40,000 cycles over 8 months with a specific capacity retention of 100%at a high current density of 2000 mA g^(−1),and the target full cells deliver a high reversible specific capacity of 146 mA h g^(−1) after 2000 cycles over 2 months,with a specific capacity retention of 113%at a high current density of 500 mA g^(−1),both of which are state of the art in the field of K-ion batteries.This study might provide some insights into and potential avenues for exploration of advanced K-ion batteries with durable stability for practical applications.

Theoretical Analysis on Deflection and Bearing Capacity of Prestressed Bamboo-Steel Composite Beams

A theoretical analysis of upward deflection and midspan deflection of prestressed bamboo-steel composite beams is presented in this study.The deflection analysis considers the influences of interface slippage and shear deformation.Furthermore,the calculation model for flexural capacity is proposed considering the two stages of loading.The theoretical results are verified with 8 specimens considering different prestressed load levels,load schemes,and prestress schemes.The results indicate that the proposed theoretical analysis provides a feasible prediction of the deflection and bearing capacity of bamboo-steel composite beams.For deflection analysis,the method considering the slippage and shear deformation provides better accuracy.The theoretical method for bearing capacity matches well with the test results,and the relative errors in the serviceability limit state and ultimate limit state are 4.95%and 5.85%,respectively,which meet the accuracy requirements of the engineered application.

An improved analysis method for assessing the nuclear-heating impact on the stability of toroidal field magnets in fusion reactors

The superconducting magnet system of a fusion reactor plays a vital role in plasma confinement,a process that can be dis-rupted by various operational factors.A critical parameter for evaluating the temperature margin of superconducting magnets during normal operation is the nuclear heating caused by D-T neutrons.This study investigates the impact of nuclear heat-ing on a superconducting magnet system by employing an improved analysis method that combines neutronics and thermal hydraulics.In the magnet system,toroidal field(TF)magnets are positioned closest to the plasma and bear the highest nuclear-heat load,making them prime candidates for evaluating the influence of nuclear heating on stability.To enhance the modeling accuracy and facilitate design modifications,a parametric TF model that incorporates heterogeneity is established to expedite the optimization design process and enhance the accuracy of the computations.A comparative analysis with a homogeneous TF model reveals that the heterogeneous model improves accuracy by over 12%.Considering factors such as heat load,magnetic-field strength,and cooling conditions,the cooling circuit facing the most severe conditions is selected to calculate the temperature of the superconductor.This selection streamlines the workload associated with thermal-hydraulic analysis.This approach enables a more efficient and precise evaluation of the temperature margin of TF magnets.Moreover,it offers insights that can guide the optimization of both the structure and cooling strategy of superconducting magnet systems.

Effective Capacity of URLLC over Parallel Fading Channels with Imperfect Channel State Information

This paper investigates the effective capacity of a point-to-point ultra-reliable low latency communication(URLLC)transmission over multiple parallel sub-channels at finite blocklength(FBL)with imperfect channel state information(CSI).Based on reasonable assumptions and approximations,we derive the effective capacity as a function of the pilot length,decoding error probability,transmit power and the sub-channel number.Then we reveal significant impact of the above parameters on the effective capacity.A closed-form lower bound of the effective capacity is derived and an alternating optimization based algorithm is proposed to find the optimal pilot length and decoding error probability.Simulation results validate our theoretical analysis and show that the closedform lower bound is very tight.In addition,through the simulations of the optimized effective capacity,insights for pilot length and decoding error probability optimization are provided to evaluate the optimal parameters in realistic systems.

MOF-derived porous graphitic carbon with optimized plateau capacity and rate capability for high performance lithium-ion capacitors

The development of anode materials with high rate capability and long charge-discharge plateau is the key to improve per-formance of lithium-ion capacitors(LICs).Herein,the porous graphitic carbon(PGC-1300)derived from a new triply interpenetrated co-balt metal-organic framework(Co-MOF)was prepared through the facile and robust carbonization at 1300°C and washing by HCl solu-tion.The as-prepared PGC-1300 featured an optimized graphitization degree and porous framework,which not only contributes to high plateau capacity(105.0 mAh·g^(−1)below 0.2 V at 0.05 A·g^(−1)),but also supplies more convenient pathways for ions and increases the rate capability(128.5 mAh·g^(−1)at 3.2 A·g^(−1)).According to the kinetics analyses,it can be found that diffusion regulated surface induced capa-citive process and Li-ions intercalation process are coexisted for lithium-ion storage.Additionally,LIC PGC-1300//AC constructed with pre-lithiated PGC-1300 anode and activated carbon(AC)cathode exhibited an increased energy density of 102.8 Wh·kg^(−1),a power dens-ity of 6017.1 W·kg^(−1),together with the excellent cyclic stability(91.6%retention after 10000 cycles at 1.0 A·g^(−1)).

Tensile Strain Capacity Prediction of Engineered Cementitious Composites (ECC) Using Soft Computing Techniques

Plain concrete is strong in compression but brittle in tension,having a low tensile strain capacity that can significantly degrade the long-term performance of concrete structures,even when steel reinforcing is present.In order to address these challenges,short polymer fibers are randomly dispersed in a cement-based matrix to forma highly ductile engineered cementitious composite(ECC).Thismaterial exhibits high ductility under tensile forces,with its tensile strain being several hundred times greater than conventional concrete.Since concrete is inherently weak in tension,the tensile strain capacity(TSC)has become one of the most extensively researched properties.As a result,developing a model to predict the TSC of the ECC and to optimize the mixture proportions becomes challenging.Meanwhile,the effort required for laboratory trial batches to determine the TSC is reduced.To achieve the research objectives,five distinct models,artificial neural network(ANN),nonlinear model(NLR),linear relationship model(LR),multi-logistic model(MLR),and M5P-tree model(M5P),are investigated and employed to predict the TSCof ECCmixtures containing fly ash.Data from115 mixtures are gathered and analyzed to develop a new model.The input variables include mixture proportions,fiber length and diameter,and the time required for curing the various mixtures.The model’s effectiveness is evaluated and verified based on statistical parameters such as R2,mean absolute error(MAE),scatter index(SI),root mean squared error(RMSE),and objective function(OBJ)value.Consequently,the ANN model outperforms the others in predicting the TSC of the ECC,with RMSE,MAE,OBJ,SI,and R2 values of 0.42%,0.3%,0.33%,0.135%,and 0.98,respectively.

Exogenous calcium enhances the physiological status and photosynthetic capacity of rose under drought stress

Drought(water shortage)can substantially limit the yield and economic value of rose plants(Rosa spp.).Here,we characterized the effect of exogenous calcium(Ca^(2+))on the antioxidant system and photosynthesis-related properties of rose under polyethylene glycol 6000(PEG6000)-induced drought stress.Chlorophyll levels,as well as leaf and root biomass,were significantly reduced by drought;drought also had a major effect on the enzymatic antioxidant system and increased concentrations of reactive oxygen species.Application of exogenous Ca^(2+)increased the net photosynthetic rate and stomatal conductance of leaves,enhanced water-use efficiency,and increased the length and width of stomata following exposure to drought.Organ-specific physiological responses were observed under different concentrations of Ca^(2+).Application of 5 mmol·L^(-1)Ca^(2+)promoted photosynthesis and antioxidant activity in the leaves,and application of 10 mmol·L^(-1)Ca^(2+)promoted antioxidant activity in the roots.Application of exogenous Ca^(2+)greatly enhanced the phenotype and photosynthetic capacity of potted rose plants following exposure to drought stress.Overall,our findings indicate that the application of exogenous Ca^(2+)enhances the drought resistance of roses by promoting physiological adaptation and that it could be used to aid the cultivation of rose plants.

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.