Investigation on a Non-cyanide Plating Process of Ni-P Coating on Magnesium Alloy AZ91D

In this research we presented a non-cyanide plating process of Ni-P alloy coating on Mg alloy AZ91D. By applying a new process flow of electroless nickel plating in which zinc coating is used as transition of Ni-P coating on Mg alloy AZ91D, the process of copper transition coating plated in the cyanides bath can be replaced. A new bath composed of NiSO4 was established by orthogonal test. The results show that zinc transition coating can increase the adhesion and protect the Mg alloy substrate from the bath corrosion. The optimal plating bath composition is NiSO4·6H2O 20 g/L, NaH2PO2·H2O20g/L and C6H8O7·H2O 2.5 g/L, and optimal bath acidity and optimal plating temperature are pH 4.0 and 95℃, respectively. The present process flow is composed of ultrasonic cleaning→alkaline cleaning→acid pickling→activation→double immersing zinc→electroplating zinc→electroless nickel plating→passivation treatment. The present non-cyanide process of electroless nickel plating is harmless to our surroundings and Ni-P coating on Mg alloy AZ91D produced by present process possesses good adhesion and corrosion resistance.

Recent developments in selective laser processes for wearable devices

Recently,the increasing interest in wearable technology for personal healthcare and smart virtual/augmented reality applications has led to the development of facile fabrication methods.Lasers have long been used to develop original solutions to such challenging technological problems due to their remote,sterile,rapid,and site-selective processing of materials.In this review,recent developments in relevant laser processes are summarized under two separate categories.First,transformative approaches,such as for laser-induced graphene,are introduced.In addition to design optimization and the alteration of a native substrate,the latest advances under a transformative approach now enable more complex material compositions and multilayer device configurations through the simultaneous transformation of heterogeneous precursors,or the sequential addition of functional layers coupled with other electronic elements.In addition,the more conventional laser techniques,such as ablation,sintering,and synthesis,can still be used to enhance the functionality of an entire system through the expansion of applicable materials and the adoption of new mechanisms.Later,various wearable device components developed through the corresponding laser processes are discussed,with an emphasis on chemical/physical sensors and energy devices.In addition,special attention is given to applications that use multiple laser sources or processes,which lay the foundation for the all-laser fabrication of wearable devices.

NADARAYA-WATSON ESTIMATORS FOR REFLECTED STOCHASTIC PROCESSES

We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed process,are considered.Under certain conditions,we prove the strong consistency and the asymptotic normality of the two estimators.Our method is also suitable for one-sided reflected stochastic differential equations.Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.

Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

An Interpretable Light Attention-Convolution-Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.

Correlation of Microbiological Stability with Redox Processes in White Wines

In this paper, the authors analyzed the correlation between the microbiological stability of white wines and the content of sulfur dioxide, which influences the main redox processes that take place in the technological stages of the wine. The consecutive, parallel and spontaneous development of several redox processes and their impact on the quality, microbiological and crystalline stability of white wines were examined. The reduction of additive and subtractive technological interventions, of the amounts of adjuvants (sulphurous anhydride) is essential for the production of organic wines.

Numerical Study on the Impacts of Hydrometeor Processes on the“21·7”Extreme Rainfall in Zhengzhou Area of China

The impacts of hydrometeor-related processes on the development and evolution of the“21·7”extremely heavy rainfall in Zhengzhou were investigated using WRF simulations.Surface precipitation was determined by the hydrometeor microphysical processes(all microphysical source sink terms of hydrometeors)and macrophysical processes(local change and flux convergence of hydrometeors).The contribution of hydrometeor macrophysical processes was commonly less than 10%,but could reach 30%–50%in the early stage of precipitation,which was largely dependent on the size of the study area.The macrophysical processes of liquid-phase hydrometeors always presented a promotional effect on rainfall,while the ice-phase hydrometeors played a negative role in the middle and later stages of precipitation.The distributions of microphysical latent heat corresponded well with those of buoyancy and vertical velocity(tendency),indicating that the phase-change heating was the major driver for convective development.Reasonable diagnostic buoyancy was obtained by choosing an area close to the convective size for getting the reference state of air.In addition,a new dynamic equilibrium involving hydrometeors with a tilted airflow was formed during the heavy precipitation period(updraft was not the strongest).The heaviest instantaneous precipitation was mainly produced by the warm-rain processes.Sensitivity experiments further pointed out that the uncertainty of latent heat parameterization(±20%)did not significantly affect the convective rainfall.While when the phase-change heating only altered the temperature tendency,its impact on precipitation was remarkable.The results of this study help to deepen our understanding of heavy rainfall mechanisms from the perspective of hydrometeor processes.

Hausdorff Dimension of Range and Graph for General Markov Processes

We establish the Hausdorff dimension of the graph of general Markov processes on Rd based on some probability estimates of the processes staying or leaving small balls in small time.In particular,our results indicate that,for symmetric diffusion processes(withα=2)or symmetricα-stable-like processes(withα∈(0,2))on Rd,it holds almost surely that dimH GrX([0,1])=1{α<1}+(2−1/α)1{α≥1,d=1}+(d∧α)1{α≥1,d≥2}.We also systematically prove the corresponding results about the Hausdorff dimension of the range of the processes.

THE EXTREMES OF DEPENDENT CHI-PROCESSES ATTRACTED BY THE BROWN-RESNICK PROCESS

Motivated by some recent works on the topic of the Brown-Resnick process, we study the functional limit theorem for normalized pointwise maxima of dependent chi-processes. It is proven that the properly normalized pointwise maxima of those processes are attracted by the Brown-Resnick process.

Modeling and analysis of air combustion and steam regeneration in methanol to olefins processes

Light olefins is the incredibly important materials in chemical industry.Methanol to olefins(MTO),which provides a non-oil route for light olefins production,received considerable attention in the past decades.However,the catalyst deactivation is an inevitable feature in MTO processes,and regeneration,therefore,is one of the key steps in industrial MTO processes.Traditionally the MTO catalyst is regenerated by removing the deposited coke via air combustion,which unavoidably transforms coke into carbon dioxide and reduces the carbon utilization efficiency.Recent study shows that the coke species over MTO catalyst can be regenerated via steam,which can promote the light olefins yield as the deactivated coke species can be essentially transferred to industrially useful synthesis gas,is a promising pathway for further MTO processes development.In this work,we modelled and analyzed these two MTO regeneration methods in terms of carbon utilization efficiency and technology economics.As shown,the steam regeneration could achieve a carbon utilization efficiency of 84.31%,compared to 74.74%for air combustion regeneration.The MTO processes using steam regeneration can essentially achieve the near-zero carbon emission.In addition,light olefins production of the MTO processes using steam regeneration is 12.81%higher than that using air combustion regeneration.In this regard,steam regeneration could be considered as a potential yet promising regeneration method for further MTO processes,showing not only great environmental benefits but also competitive economic performance.

Causal temporal graph attention network for fault diagnosis of chemical processes

Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.

Multi-Perspective Data Fusion Framework Based on Hierarchical BERT: Provide Visual Predictions of Business Processes

Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral events.At present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process prediction.Therefore,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion.Firstly,the first layer BERT network learns the correlations between different category attribute data.Then,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted events.Next,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual predictions.Finally,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.

Rosgen stream classification and fluvial processes of the Shiyang River,China

The Shiyang River is an important ecological pillar in northwest China,sustaining Minqin oasis and its surrounding society.However,the basin has long been plagued by water scarcity and ecological fragility.Although the river classification is critical for understanding the complexity,diversity,and ecological functions of rivers,and the foundation of river management and watershed ecological restoration,it has not received adequate attention in this region.To obtain a deeper and comprehensive understanding of the Shiyang River,this study utilizes the Rosgen stream classification system to assess the river morphology,geomorphic features,and hydrologic processes.The results showed that seven first-level and fourteen second-level river types can be identified along 53 river sections of the Shiyang River.Further comparison analysis on the hydrologic parameters for each river type demonstrated a strong positive correlation between discharge and all river parameters.As discharge increased,channels with moderate to high width/depth ratios experienced significant lateral adjustments.A consistent channel gradient,coupled with higher discharge,facilitated the transition from single to multiple channels.Braiding tendencies were more pronounced in rivers where riverbeds were wider and shallower with higher stream power.Additionally,water-flow shear stress decreased with the increase in the width/depth ratio.This study offered critical insights into the Shiyang River’s forms and processes and for the river management and ecological restoration practices.

Boosting Peroxymonosulfate Activation via Co-Based LDH-Derived Magnetic Catalysts:A Dynamic and Static State Assessment of Efficient Radical-Assisted Electron Transfer Processes

Heterogeneous catalysts promoting efficient production of reactive species and dynamically stabilized electron transfer mechanisms for peroxomonosulfates(PMS)still lack systematic investigation.Herein,a more stable magnetic layered double oxides(CFLDO/N-C),was designed using self-polymerization and high temperature carbonization of dopamine.The CFLDO/N-C/PMS system effectively activated PMS to remove 99%(k=0.737 min^(-1))of tetracycline(TC)within 10 min.The CFLDO/N-C/PMS system exhibited favorable resistance to inorganic anions and natural organics,as well as satisfactory suitability for multiple pollutants.The magnetic properties of the catalyst facilitated the separation of catalysts from the liquid phase,resulting in excellent reproducibility and effectively reducing the leaching of metal ions.An electronic bridge was constructed between cobalt(the active platform of the catalyst)and PMS,inducing PMS to break the O-O bond to generate the active species.The combination of static analysis and dynamic evolution confirmed the effective adsorption of PMS on the catalyst surface as well as the strong radical-assisted electron transfer process.Eventually,we further identified the sites where the reactive species attacked the TC and evaluated the toxicity of the intermediates.These findings offer innovative insights into the rapid degradation of pollutants achieved by transition metals in SR-AOPs and its mechanistic elaboration.

Local Torrential Rainfall Event within a Mei-Yu Season Mesoscale Convective System:Importance of Back-Building Processes

An extreme rainfall event occurred over Hangzhou,China,during the afternoon hours on 24 June 2013.This event occurred under suitable synoptic conditions and the maximum 4-h cumulative rainfall amount was over 150 mm.This rainfall event had two major rainbands.One was caused by a quasi-stationary convective line,and the other by a backbuilding convective line related to the interaction of the outflow boundary from the first rainband and an existing low-level mesoscale convergence line associated with a mei-yu frontal system.The rainfall event lasted 4 h,while the back-building process occurred in 2 h when the extreme rainfall center formed.So far,few studies have examined the back-building processes in the mei-yu season that are caused by the interaction of a mesoscale convergence line and a convective cold pool.The two rainbands are successfully reproduced by the Weather Research and Forecasting(WRF)model with fourlevel,two-way interactive nesting.In the model,new cells repeatedly occur at the west side of older cells,and the backbuilding process occurs in an environment with large CAPE,a low LFC,and plenty of water vapor.Outflows from older cells enhance the low-level convergence that forces new cells.High precipitation efficiency of the back-building training cells leads to accumulated precipitation of over 150 mm.Sensitivity experiments without evaporation of rainwater show that the convective cold pool plays an important role in the organization of the back-building process in the current extreme precipitation case.

Numerical Study of the Efficiency of Multi-Layer Membrane Filtration in Desalination Processes

Multi-layer membrane filtration is a widely used technology for separating and purifying different components ofa liquid mixture. This technique involves passing the liquid mixture through a series of membranes with decreasing pore sizes, which allows for the separation of different components according to their molecular size. Thisstudy investigates the filtration process of a fluid through a two-dimensional porous medium designed forseawater desalination. The focus is on understanding the impact of various parameters such as the coefficientof friction, velocity, and the number of layers on filtration efficiency. The results reveal that the number of layersplays a crucial role in desalination, with an increase in layers leading to enhanced filtration quality, following apower law relationship. The study explores the influence of the coefficient of friction on filtration performance,emphasizing its significant effect on the number of particles filtered over time. Additionally, the role of the initialvelocity in filtration efficiency is examined, showing distinct effects at both high and low velocities. Biofouling isidentified as a factor influencing filtration, with an initial increase in filtered particles followed by a decline due toparticle accumulation in pores.

Comparison of a Spectral Bin and Two Multi-Moment Bulk Microphysics Schemes for Supercell Simulation:Investigation into Key Processes Responsible for Hydrometeor Distributions and Precipitation

There are more uncertainties with ice hydrometeor representations and related processes than liquid hydrometeors within microphysics parameterization(MP)schemes because of their complicated geometries and physical properties.Idealized supercell simulations are produced using the WRF model coupled with“full”Hebrew University spectral bin MP(HU-SBM),and NSSL and Thompson bulk MP(BMP)schemes.HU-SBM downdrafts are typically weaker than those of the NSSL and Thompson simulations,accompanied by less rain evaporation.HU-SBM produces more cloud ice(plates),graupel,and hail than the BMPs,yet precipitates less at the surface.The limiting mass bins(and subsequently,particle size)of rimed ice in HU-SBM and slower rimed ice fall speeds lead to smaller melting-level net rimed ice fluxes than those of the BMPs.Aggregation from plates in HU-SBM,together with snow–graupel collisions,leads to a greater snow contribution to rain than those of the BMPs.Replacing HU-SBM’s fall speeds using the formulations of the BMPs after aggregating the discrete bin values to mass mixing ratios and total number concentrations increases net rain and rimed ice fluxes.Still,they are smaller in magnitude than bulk rain,NSSL hail,and Thompson graupel net fluxes near the surface.Conversely,the melting-layer net rimed ice fluxes are reduced when the fall speeds for the NSSL and Thompson simulations are calculated using HU-SBM fall speed formulations after discretizing the bulk particle size distributions(PSDs)into spectral bins.The results highlight precipitation sensitivity to storm dynamics,fall speed,hydrometeor evolution governed by process rates,and MP PSD design.

Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development

With the projected global surge in hydrogen demand, driven by increasing applications and the imperative for low-emission hydrogen, the integration of machine learning(ML) across the hydrogen energy value chain is a compelling avenue. This review uniquely focuses on harnessing the synergy between ML and computational modeling(CM) or optimization tools, as well as integrating multiple ML techniques with CM, for the synthesis of diverse hydrogen evolution reaction(HER) catalysts and various hydrogen production processes(HPPs). Furthermore, this review addresses a notable gap in the literature by offering insights, analyzing challenges, and identifying research prospects and opportunities for sustainable hydrogen production. While the literature reflects a promising landscape for ML applications in hydrogen energy domains, transitioning AI-based algorithms from controlled environments to real-world applications poses significant challenges. Hence, this comprehensive review delves into the technical,practical, and ethical considerations associated with the application of ML in HER catalyst development and HPP optimization. Overall, this review provides guidance for unlocking the transformative potential of ML in enhancing prediction efficiency and sustainability in the hydrogen production sector.

Quantification of irrigation water transport processes in ZiZiphus jujuba garden using water stable isotopes

ZiZiphus jujuba,which is native to China,has become one of the main crops widely planted in the western Loess Plateau because of its drought and flood-tolerance,adaptability,and higher nutritional value of the fruit.The irrigation water infiltration in Z.jujuba gardens is complex,and understanding its mechanisms is essential for efficient water use and sustainable agriculture.This knowledge helps ensure the long-term success of jujuba cultivation.This paper describes a field experiment that investigates the infiltration process of irrigation water from Z.jujuba garden and quantifies the contribution of irrigation water to soil water at different depths using the MixSIAR model.According to the FC(Field water holding Capacity)of Z.jujuba,irrigation experiments with three volumes of 80%FC,60%FC,and 40%FC are set up in this study.The study finds that water retention is better in Z.jujuba garden soils with a higher proportion of coarse gravel in the soil particle composition.Soil water content exhibits a gradient change after irrigation,with deeper wetting front transport depth observed with increased irrigation water.Additionally,there is correlation between soil temperature and soil water content.The soil water in Z.jujuba garden generally exhibits a preferential flow signal in the 0-40 cm range.Below 40 cm,a piston flow pattern dominates.The rate of soil water infiltration increases with the amount of irrigation water.In the 0-40 cm range of the soil vertical profile,irrigation water was the main contributor to soil water.Z.jujuba demonstrated flexibility in water uptake,primarily absorbing soil water at depths of 0-40 cm.For optimal growth of Z.jujuba at this stage,40%FC irrigation is recommended.The results are expected to be valuable future irrigation practices and land use planning for Z.jujuba garden in arid zones,supporting sustainable agricultural development and water management.

Mass Transfer-Promoted Fe^(2+)/Fe^(3+)Circulation Steered by 3D Flow-Through Co-Catalyst System Toward Sustainable Advanced Oxidation Processes

Realizing fast and continuous generation of reactive oxygen species(ROSs)via iron-based advanced oxidation processes(AOPs)is significant in the environmental and biological fields.However,current AOPs assisted by co-catalysts still suffer from the poor mass/electron transfer and non-durable promotion effect,giving rise to the sluggish Fe^(2+)/Fe^(3+)cycle and low dynamic concentration of Fe^(2+)for ROS production.Herein,we present a three-dimensional(3D)macroscale co-catalyst functionalized with molybdenum disulfide(MoS_(2))to achieve ultra-efficient Fe^(2+)regeneration(equilibrium Fe^(2+)ratio of 82.4%)and remarkable stability(more than 20 cycles)via a circulating flow-through process.Unlike the conventional batch-type reactor,experiments and computational fluid dynamics simulations demonstrate that the optimal utilization of the 3D active area under the flow-through mode,initiated by the convectionenhanced mass/charge transfer for Fe^(2+)reduction and then strengthened by MoS_(2)-induced flow rotation for sufficient reactant mixing,is crucial for oxidant activation and subsequent ROS generation.Strikingly,the flow-through co-catalytic system with superwetting capabilities can even tackle the intricate oily wastewater stabilized by different surfactants without the loss of pollutant degradation efficiency.Our findings highlight an innovative co-catalyst system design to expand the applicability of AOPs based technology,especially in large-scale complex wastewater treatment.