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.

Two-Stage Optimal Scheduling of Community Integrated Energy System

From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.

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.

Two-Stage Planning of Distributed Power Supply and Energy Storage Capacity Considering Hierarchical Partition Control of Distribution Network with Source-Load-Storage

Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning.

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.

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.

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.

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.

Solid-state impedance spectroscopy studies of dielectric properties and relaxation processes in Na_(2)O–V_(2)O_(5)–Nb_(2)O_(5)–P_(2)O_(5) glass

Solid-state impedance spectroscopy(SS-IS)was used to investigate the influence of structural modifications resulting from the addition of Nb2O5 on the dielectric properties and relaxation processes in the quaternary mixed glass former(MGF)system 35Na_(2)O–10V_(2)O_(5)–(55-x)P_(2)O_(5)–xNb_(2)O_(5)(x=0–40,mol%).The dielectric parameters,including the dielectric strength and dielectric loss,are determined from the frequency and temperature-dependent complex permittivity data,revealing a significant dependence on the Nb2O5 content.The transition from a predominantly phosphate glass network(x<10,region I)to a mixed niobate–phosphate glass net-work(10≤x≤20,region II)leads to an increase in the dielectric parameters,which correlates with the observed trend in the direct-cur-rent(DC)conductivity.In the predominantly niobate network(x≥25,region III),the highly polarizable nature of Nb5+ions leads to a fur-ther increase in the dielectric permittivity and dielectric strength.This is particularly evident in Nb-40 glass-ceramic,which contains Na_(13)Nb_(35)O_(94) crystalline phase with a tungsten bronze structure and exhibits the highest dielectric permittivity of 61.81 and the lowest loss factor of 0.032 at 303 K and 10 kHz.The relaxation studies,analyzed through modulus formalism and complex impedance data,show that DC conductivity and relaxation processes are governed by the same mechanism,attributed to ionic conductivity.In contrast to glasses with a single peak in frequency dependence of imaginary part of electrical modulus,M″(ω),Nb-40 glass-ceramic exhibits two distinct contributions with similar relaxation times.The high-frequency peak indicates bulk ionic conductivity,while the additional low-fre-quency peak is associated with the grain boundary effect,confirmed by the electrical equivalent circuit(EEC)modelling.The scaling characteristics of permittivity and conductivity spectra,along with the electrical modulus,validate time-temperature superposition and demonstrate a strong correlation with composition and modification of the glass structure upon Nb_(2)O_(5) incorporation.

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.

Challenges and opportunities in the production of magnesium parts by directed energy deposition processes

Mg-alloys have gained considerable attention in recent years for their outstanding properties such as lightweight,high specific strength,and corrosion resistance,making them attractive for applications in medical,aerospace,automotive,and other transport industries.However,their widespread application is hindered by their low formability at room temperature due to limited slip systems.Cast Mg-alloys have low mechanical properties due to the presence of casting defects such as porosity and anisotropy in addition to the high scrap.While casting methods benefit from established process optimization techniques for these problems,additive manufacturing methods are increasingly replacing casting methods in Mg alloys as they provide more precise control over the microstructure and allow specific grain orientations,potentially enabling easier optimization of anisotropy properties in certain applications.Although metal additive manufacturing(MAM)technology also results in some manufacturing defects such as inhomogeneous microstructural evolution and porosity and additively manufactured Mg alloy parts exhibit lower properties than the wrought parts,they in general exhibit superior properties than the cast counterparts.Thus,MAM is a promising technique to produce Mg alloy parts.Directed energy deposition processes,particularly wire arc directed energy deposition(WA-DED),have emerged as an advantageous additive manufacturing(AM)technique for metallic materials including magnesium alloys,offering advantages such as high deposition rates,improved material efficiency,and reduced production costs compared to subtractive processes.However,the inherent challenges associated with magnesium,such as its high reactivity and susceptibility to oxidation,pose unique hurdles in the application of this technology.This review paper delves into the progress made in the application of DED technology to Mg-alloys,its challenges,and prospects.Furthermore,the predominant imperfections,notably inhomogeneous microstructure evolution and porosity,observed in Mg-alloy components manufactured through DED are discussed.Additionally,the preventive measures implemented to counteract the formation of these defects are explored.

UV-Based Advanced Oxidation Processes for Antibiotic Resistance Control: Efficiency, Influencing Factors, and Energy Consumption

Antibiotic resistant bacteria(ARB)with antibiotic resistance genes(ARGs)can reduce or eliminate the effectiveness of antibiotics and thus threaten human health.The United Nations Environment Programme considers antibiotic resistance the first of six emerging issues of concern.Advanced oxidation processes(AOPs)that combine ultraviolet(UV)irradiation and chemical oxidation(primarily chlorine,hydrogen peroxide,and persulfate)have attracted increasing interest as advanced water and wastewater treatment technologies.These integrated technologies have been reported to significantly elevate the efficiencies of ARB inactivation and ARG degradation compared with direct UV irradiation or chemical oxidation alone due to the generation of multiple reactive species.In this study,the performance and underlying mechanisms of UV/chlorine,UV/hydrogen peroxide,and UV/persulfate processes for controlling ARB and ARGs were reviewed based on recent studies.Factors affecting the process-specific efficiency in controlling ARB and ARGs were discussed,including biotic factors,oxidant dose,UV fluence,pH,and water matrix properties.In addition,the cost-effectiveness of the UV-based AOPs was evaluated using the concept of electrical energy per order.The UV/chlorine process exhibited a higher efficiency with lower energy consumption than other UV-based AOPs in the wastewater matrix,indicating its potential for ARB inactivation and ARG degradation in wastewater treatment.Further studies are required to address the trade-off between toxic byproduct formation and the energy efficiency of the UV/chlorine process in real wastewater to facilitate its optimization and application in the control of ARB and ARGs.

Impact of Optimizing Emergency Nursing Processes on Resuscitation Success in Patients with Acute Chest Pain

Objective:To analyze the effect of optimizing the emergency nursing process in the resuscitation of patients with acute chest pain and the impact on the resuscitation success rate.Methods:66 patients with acute chest pain received by the emergency department of our hospital from January 2022 to December 2023 were selected as the study subjects and divided into two groups according to the differences in the emergency nursing process,i.e.,33 patients receiving routine emergency care were included in the control group,and 33 patients receiving the optimization of emergency nursing process intervention were included in the observation group.Patients’resuscitation effect and satisfaction with nursing care in the two groups were compared.Results:The observation group’s consultation assessment time,reception time,admission to the start of resuscitation time,and resuscitation time were shorter than that of the control group,the resuscitation success rate was higher than that of the control group,and the incidence of adverse events was lower than that of the control group,with statistically significant differences(P<0.05);and the observation group’s satisfaction with nursing care was higher than that of the control group,with statistically significant differences(P<0.05).Conclusion:Optimization of emergency nursing process intervention in the resuscitation of acute chest pain patients can greatly shorten the rescue time and improve the success rate of resuscitation,with higher patient satisfaction.

Reliability modelling based on dependent two-stage virtual age processes

This paper proposes reliability and maintenance models for systems suffering random shocks arriving according to a non-homogeneous Poisson process.The system degradation process include two stages:from the installation of a new system to an initial point of a defect(normal stage),and then from that point to failure(defective stage),following the delay time concept.By employing the virtual age method,the impact of external shocks on the system degradation process is characterized by random virtual age increment in the two stages,resulting in the corresponding two-stage virtual age process.When operating in the defective state,the system becomes more susceptible to fatigue and suffers from a greater aging rate.Replacement is carried out either on failure or on the detection of a defective state at periodic or opportunistic inspections.This paper evaluates system reliability performance and investigates the optimal opportunistic maintenance policy.A case study on a cooling system is given to verify the obtained results.

Influence of two-stage harvesting on the properties of cold-pressed rapeseed(Brassica napus L.)oils

Rapeseed(Brassica napus L.)harvesting method is critical since it significantly determines the seed yield,oil quality,and industrial efficiency.This study investigated the influences of harvesting methods on the quality of cold-pressed rapeseed oil of two varieties.Oil color,peroxide value(POV),tocopherol content,fatty acid composition,and polarity of total polyphenols(PTP)contents of two rapeseed varieties in Huanggang and Xiangyang were compared through artificially simulated combined harvesting and two-stage harvesting.Results showed significant differences in the quality of rapeseed oil between the two harvesting methods.The red value(R-value),POV,total tocopherol contents,linoleic and linolenic acid content,and PTP content of the pressed rapeseed oil prepared by the combined harvesting method were about 27.6,5.7,15.8,2.0,0.5,and 28.6%lower than those of the oil produced from the two-stage harvesting method,respectively.Xiangyang and Huayouza62 performed better in the two regions and two varieties,respectively.To sum up,the rapeseed oil obtained 41–44 days after final flowering of combined harvesting,35 days after final flowering,and six days of post-ripening of the two-stage harvesting had the best quality.

On the Key Dynamical Processes Supporting the 21.7 Zhengzhou Record-breaking Hourly Rainfall in China

An extremely heavy rainfall event occurred in Zhengzhou,China,on 20 July 2021 and produced an hourly rainfall rate of 201.9 mm,which broke the station record for China's Mainland.Based on radar observations and a convection-permitting simulation using the WRF-ARW model,this paper investigates the multiscale processes,especially those at the mesoscale,that support the extreme observed hourly rainfall.Results show that the extreme rainfall occurred in an environment characteristic of warm-sector heavy rainfall,with abundant warm moist air transported from the ocean by an abnormally northward-displaced western Pacific subtropical high and Typhoon In-Fa(2021).However,rather than through back building and echo training of convective cells often found in warm-sector heavy rainfall events,this extreme hourly rainfall event was caused by a single,quasi-stationary storm in Zhengzhou.Scale separation analysis reveals that the extreme-rainproducing storm was supported and maintained by the dynamic lifting of low-level converging flows from the north,south,and east of the storm.The low-level northerly flow originated from a mesoscale barrier jet on the eastern slope of the Taihang Mountain due to terrain blocking of large-scale easterly flows,which reached an overall balance with the southerly winds in association with a low-level meso-β-scale vortex located to the west of Zhengzhou.The large-scale easterly inflows that fed the deep convection via transport of thermodynamically unstable air into the storm prevented the eastward propagation of the weak,shallow cold pool.As a result,the convective storm was nearly stationary over Zhengzhou,resulting in record-breaking hourly precipitation.