The Mechanism of Heating Rate on the Secondary Recrystallization Evolution in Grain Oriented Silicon Steel

Grain-oriented silicon steels were prepared at different heating rates during high temperature annealing,in which the evolution of magnetic properties,grain orientations and precipitates were studied.To illustrate the Zener factor,the diameter and number density of precipitates of interrupted testing samples were statistically calculated.The effect of precipitate ripening on the Goss texture and magnetic property was investigated.Data indicated that the trend of Zener factor was similar under different heating rates,first increasing and then decreasing,and that the precipitate maturing was greatly inhibited as the heating rate increased.Secondary recrystallization was developed at the temperature of 1010℃when a heating rate of 5℃/h was used,resulting in Goss,Brass and{110}<227>oriented grains growing abnormally and a magnetic induction intensity of 1.90T.Furthermore,increasing the heating rate to 20℃/h would inhibit the development of undesirable oriented grains and obtain a sharp Goss texture.However,when the heating rate was extremely fast,such as 40℃/h,poor secondary recrystallization was developed with many island grains,corresponding to a decrease in magnetic induction intensity to 1.87 T.At a suitable heating rate of 20℃/h,the sharpest Goss texture and the highest magnetic induction of 1.94 T with an onset secondary recrystallization temperature of 1020℃were found among the experimental variables in this study.The heating rate affected the initial temperature of secondary recrystallization by controlling the maturation of precipitates,leading to the deviation and dispersion of Goss texture,thereby reducing the magnetic properties.

Prediction and optimization of flue pressure in sintering process based on SHAP

Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.

Prediction of Shear Bond Strength of Asphalt Concrete Pavement Using Machine Learning Models and Grid Search Optimization Technique

Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.

Importance of etiologies of secondary diabetes:How often do we think off in clinical practice?

The article"Secondary diabetes due to different etiologies:Four case reports"by Song et al,published in the World Journal of Clinical Cases,delves into the identi-fication of rare causes of secondary diabetes and emphasizes the necessity for healthcare professionals to recognize these conditions.Failure to do so can result in treatment delays and compromised patient outcomes.The article discusses spe-cific types of diabetes,including maturity onset of diabetes in young,pancreas-related diseases,endocrinopathies,drug-induced diabetes,infections,and con-genital genetic syndromes associated with diabetes mellitus.Case summaries highlight how patients with secondary diabetes,stemming from conditions such as Williams-Beuren syndrome and pituitary adenoma,often exhibit distinct characteristics overlooked in clinical practice.The authors stress the importance of a holistic diagnostic approach and advocate for proactive management through early intervention,including genetic tests and antibody detection.Increased awa-reness and education are crucial for timely identification and proper management,ultimately improving patient well-being.These findings prompt a call to action for healthcare professionals to consider rare causes of secondary diabetes,facili-tating better glycemic control and overall patient care.

Role of triggering receptor expressed on myeloid cells 1/2 in secondary injury after cerebral hemorrhage

Intracerebral hemorrhage(ICH)is a common severe emergency in neurosurgery,causing tremendous economic pressure on families and society and devastating effects on patients both physically and psychologically,especially among patients with poor functional outcomes.ICH is often accompanied by decreased consciousness and limb dysfunction.This seriously affects patients’ability to live independently.Although rapid advances in neurosurgery have greatly improved patient survival,there remains insufficient evidence that surgical treatment significantly improves long-term outcomes.With in-depth pathophysiological studies after ICH,increasing evidence has shown that secondary injury after ICH is related to long-term prognosis and that the key to secondary injury is various immune-mediated neuroinflammatory reactions after ICH.In basic and clinical studies of various systemic inflammatory diseases,triggering receptor expressed on myeloid cells 1/2(TREM-1/2),and the TREM receptor family is closely related to the inflammatory response.Various inflammatory diseases can be upregulated and downregulated through receptor intervention.How the TREM receptor functions after ICH,the types of results from intervention,and whether the outcomes can improve secondary brain injury and the long-term prognosis of patients are unknown.An analysis of relevant research results from basic and clinical trials revealed that the inhibition of TREM-1 and the activation of TREM-2 can alleviate the neuroinflammatory immune response,significantly improve the long-term prognosis of neurological function in patients with cerebral hemorrhage,and thus improve the ability of patients to live independently.

Topology, Size, and Shape Optimization in Civil Engineering Structures: A Review

The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural safety.The design approaches aim to reduce the built environment’s energy use and carbon emissions.This comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these methodologies.The trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were identified.The review also discusses emerging technologies,such as machine learning applications with different optimization techniques.Optimization of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and discussed.Optimization techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure types.Linear and non-linear structures,including geometric and material nonlinearity,are distinguished.The role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.

Electrode/Electrolyte Optimization‑Induced Double‑Layered Architecture for High‑Performance Aqueous Zinc‑(Dual)Halogen Batteries

Aqueous zinc-halogen batteries are promising candidates for large-scale energy storage due to their abundant resources,intrinsic safety,and high theoretical capacity.Nevertheless,the uncontrollable zinc dendrite growth and spontaneous shuttle effect of active species have prohibited their practical implementation.Herein,a double-layered protective film based on zinc-ethylenediamine tetramethylene phosphonic acid(ZEA)artificial film and ZnF2-rich solid electrolyte interphase(SEI)layer has been successfully fabricated on the zinc metal anode via electrode/electrolyte synergistic optimization.The ZEA-based artificial film shows strong affinity for the ZnF2-rich SEI layer,therefore effectively suppressing the SEI breakage and facilitating the construction of double-layered protective film on the zinc metal anode.Such double-layered architecture not only modulates Zn2+flux and suppresses the zinc dendrite growth,but also blocks the direct contact between the metal anode and electrolyte,thus mitigating the corrosion from the active species.When employing optimized metal anodes and electrolytes,the as-developed zinc-(dual)halogen batteries present high areal capacity and satisfactory cycling stability.This work provides a new avenue for developing aqueous zinc-(dual)halogen batteries.

An Algorithm for Cloud-based Web Service Combination Optimization Through Plant Growth Simulation

In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.

Multi-Objective Hybrid Sailfish Optimization Algorithm for Planetary Gearbox and Mechanical Engineering Design Optimization Problems

This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.

Enhanced Multi-Object Dwarf Mongoose Algorithm for Optimization Stochastic Data Fusion Wireless Sensor Network Deployment

Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.

Optimization Strategies of Na_(3)V_(2)(PO_(4))_(3) Cathode Materials for Sodium‑Ion Batteries

Na_(3)V_(2)(PO_(4))_(3)(NVP)has garnered great attentions as a prospective cathode material for sodium-ion batteries(SIBs)by virtue of its decent theoretical capacity,superior ion conductivity and high structural stability.However,the inherently poor electronic conductivity and sluggish sodium-ion diffusion kinetics of NVP material give rise to inferior rate performance and unsatisfactory energy density,which strictly confine its further application in SIBs.Thus,it is of significance to boost the sodium storage performance of NVP cathode material.Up to now,many methods have been developed to optimize the electrochemical performance of NVP cathode material.In this review,the latest advances in optimization strategies for improving the electrochemical performance of NVP cathode material are well summarized and discussed,including carbon coating or modification,foreign-ion doping or substitution and nanostructure and morphology design.The foreign-ion doping or substitution is highlighted,involving Na,V,and PO_(4)^(3−)sites,which include single-site doping,multiple-site doping,single-ion doping,multiple-ion doping and so on.Furthermore,the challenges and prospects of high-performance NVP cathode material are also put forward.It is believed that this review can provide a useful reference for designing and developing high-performance NVP cathode material toward the large-scale application in SIBs.

Optimization strategies for operational parameters of Rydberg atom-based amplitude modulation receiver

The Rydberg atom-based receiver, as a novel type of antenna, demonstrates broad application prospects in the field of microwave communications. However, since Rydberg atomic receivers are nonlinear systems, mismatches between the parameters of the received amplitude modulation(AM) signals and the system's linear workspace and demodulation operating points can cause severe distortion in the demodulated signals. To address this, the article proposes a method for determining the operational parameters based on the mean square error(MSE) and total harmonic distortion(THD) assessments and presents strategies for optimizing the system's operational parameters focusing on linear response characteristics(LRC) and linear dynamic range(LDR). Specifically, we employ a method that minimizes the MSE to define the system's linear workspace, thereby ensuring the system has a good LRC while maximizing the LDR. To ensure that the signal always operates within the linear workspace, an appropriate carrier amplitude is set as the demodulation operating point. By calculating the THD at different operating points, the LRC performance within different regions of the linear workspace is evaluated, and corresponding optimization strategies based on the range of signal strengths are proposed. Moreover, to more accurately restore the baseband signal, we establish a mapping relationship between the carrier Rabi frequency and the transmitted power of the probe light, and optimize the slope of the linear demodulation function to reduce the MSE to less than 0.8×10^(-4). Finally, based on these methods for determining the operational parameters, we explore the effects of different laser Rabi frequencies on the system performance, and provide optimization recommendations. This research provides robust support for the design of high-performance Rydberg atom-based AM receivers.

GPU-Enabled Isogometric Topology Optimization with Bezier Element Stiffness Mapping

Due to the high-order B-spline basis functions utilized in isogeometric analysis(IGA)and the repeatedly updating global stiffness matrix of topology optimization,Isogeometric topology optimization(ITO)intrinsically suffers from the computationally demanding process.In this work,we address the efficiency problem existing in the assembling stiffness matrix and sensitivity analysis using B˙ezier element stiffness mapping.The Element-wise and Interaction-wise parallel computing frameworks for updating the global stiffness matrix are proposed for ITO with B˙ezier element stiffness mapping,which differs from these ones with the traditional Gaussian integrals utilized.Since the explicit stiffness computation formula derived from B˙ezier element stiffness mapping possesses a typical parallel structure,the presented GPU-enabled ITO method can greatly accelerate the computation speed while maintaining its high memory efficiency unaltered.Numerical examples demonstrate threefold speedup:1)the assembling stiffness matrix is accelerated by 10×maximumly with the proposed GPU strategy;2)the solution efficiency of a sparse linear system is enhanced by up to 30×with Eigen replaced by AMGCL;3)the efficiency of sensitivity analysis is promoted by 100×with GPU applied.Therefore,the proposed method is a promising way to enhance the numerical efficiency of ITO for both single-patch and multiple-patch design problems.

An optimization algorithm for locomotive secondary spring load adjustment based on artificial immune

In order to control the locomotive wheel(axle) load distribution, a shimming process to adjust the locomotive secondary spring loads was heretofore developed. An immune dominance clonal selection multi-objective algorithm based on the artificial immune system was presented to further improve the performance of the optimization algorithm for locomotive secondary spring load adjustment, especially to solve the lack of control on the output shim quantity. The algorithm was designed into a two-level optimization structure according to the preferences of the problem, and the priori knowledge of the problem was used as the immune dominance. Experiments on various types of locomotives show that owing to the novel algorithm, the shim quantity is cut down by 30% 60% and the calculation time is about 90% less while the secondary spring load distribution is controlled on the same level as before. The application of this optimization algorithm can significantly improve the availability and efficiency of the secondary spring adjustment process.

Evaluation and optimization of secondary water supply system renovation

Due to pollution in second water supply system (SWSS),nine renovation alternative plans were proposed and com-prehensive evaluations of different plan based on Analytical Hierarchy Process (AHP) were presented in this paper. Comparisons of advantages and disadvantages among the plans of SWSS renovations provided solid foundation for selecting the most appro-priate plan for engineering projects. In addition,a mathematical model of the optimal combination of renovation plans has been set up and software Lingo was used to solve the model. As a case study,the paper analyzed 15 buildings in Tianjin City. After simulation of the SWSS renovation system,an optimal scheme was obtained,the result of which indicates that 10 out of those 15 buildings need be renovated in priority. The renovation plans selected for each building are the ones ranked higher in the com-prehensive analysis. The analysis revealed that the optimal scheme,compared with two other randomly calculated ones,increased the percentage of service population by 19.6% and 13.6% respectively,which significantly improved social and economical benefits.

Prediction of RNA Secondary Structure Based on Particle Swarm Optimization

A novel method for the prediction of RNA secondary structure was proposed based on the particle swarm optimization（PSO）. PSO is known to be effective in solving many different types of optimization problems and known for being able to approximate the global optimal results in the solution space. We designed an efficient objective function according to the minimum free energy, the number of selected stems and the average length of selected stems. We calculated how many legal stems there were in the sequence, and selected some of them to obtain an optimal result using PSO in the right of the objective function. A method based on the improved particle swarm optimization（IPSO） was proposed to predict RNA secondary structure, which consisted of three stages. The first stage was applied to encoding the source sequences, and to exploring all the legal stems. Then, a set of encoded stems were created in order to prepare input data for the second stage. In the second stage, IPSO was responsible for structure selection. At last, the optimal result was obtained from the secondary structures selected via IPSO. Nine sequences from the comparative RNA website were selected for the evaluation of the proposed method. Compared with other six methods, the proposed method decreased the complexity and enhanced the sensitivity and specificity on the basis of the experiment results.

Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning

It is difficult to rapidly design the process parameters of copper alloys by using the traditional trial-and-error method and simultaneously improve the conflicting mechanical and electrical properties.The purpose of this work is to develop a new type of Cu-Ni-Co-Si alloy saving scarce and expensive Co element,in which the Co content is less than half of the lower limit in ASTM standard C70350 alloy,while the properties are as the same level as C70350 alloy.Here we adopted a strategy combining Bayesian optimization machine learning and experimental iteration and quickly designed the secondary deformation-aging parameters(cold rolling deformation 90%,aging temperature 450℃,and aging time 1.25 h)of the new copper alloy with only 32 experiments(27 basic sample data acquisition experiments and 5 iteration experiments),which broke through the barrier of low efficiency and high cost of trial-and-error design of deformation-aging parameters in precipitation strengthened copper alloy.The experimental hardness,tensile strength,and electrical conductivity of the new copper alloy are HV(285±4),(872±3)MPa,and(44.2±0.7)%IACS(international annealed copper standard),reaching the property level of the commercial lead frame C70350 alloy.This work provides a new idea for the rapid design of material process parameters and the simultaneous improvement of mechanical and electrical properties.

Strigolactones modulate cotton fiber elongation and secondary cell wall thickening

Cotton is one of the most important economic crops in the world,and it is a major source of fiber in the textile industry.Strigolactones(SLs)are a class of carotenoid-derived plant hormones involved in many processes of plant growth and development,although the functions of SL in fiber development remain largely unknown.Here,we found that the endogenous SLs were significantly higher in fibers at 20 days post-anthesis(DPA).Exogenous SLs significantly increased fiber length and cell wall thickness.Furthermore,we cloned three key SL biosynthetic genes,namely GhD27,GhMAX3,and GhMAX4,which were highly expressed in fibers,and subcellular localization analyses revealed that GhD27,GhMAX3,and GhMAX4 were localized in the chloroplast.The exogenous expression of GhD27,GhMAX3,and GhMAX4 complemented the physiological phenotypes of d27,max3,and max4 mutations in Arabidopsis,respectively.Knockdown of GhD27,GhMAX3,and GhMAX4 in cotton resulted in increased numbers of axillary buds and leaves,reduced fiber length,and significantly reduced fiber thickness.These findings revealed that SLs participate in plant growth,fiber elongation,and secondary cell wall formation in cotton.These results provide new and effective genetic resources for improving cotton fiber yield and plant architecture.

The secondary injury cascade after spinal cord injury:an analysis of local cytokine/chemokine regulation

After spinal cord injury,there is an extensive infiltration of immune cells,which exacerbates the injury and leads to further neural degeneration.Therefore,a major aim of current research involves targeting the immune response as a treatment for spinal cord injury.Although much research has been performed analyzing the complex inflammatory process following spinal cord injury,there remain major discrepancies within previous literature regarding the timeline of local cytokine regulation.The objectives of this study were to establish an overview of the timeline of cytokine regulation for 2 weeks after spinal cord injury,identify sexual dimorphisms in terms of cytokine levels,and determine local cytokines that significantly change based on the severity of spinal cord injury.Rats were inflicted with either a mild contusion,moderate contusion,severe contusion,or complete transection,7 mm of spinal cord centered on the injury was harvested at varying times post-injury,and tissue homogenates were analyzed with a Cytokine/Chemokine 27-Plex assay.Results demonstrated pro-inflammatory cytokines including tumor necrosis factorα,interleukin-1β,and interleukin-6 were all upregulated after spinal cord injury,but returned to uninjured levels within approximately 24 hours post-injury,while chemokines including monocyte chemoattractant protein-1 remained upregulated for days post-injury.In contrast,several anti-inflammatory cytokines and growth factors including interleukin-10 and vascular endothelial growth factor were downregulated by 7 days post-injury.After spinal cord injury,tissue inhibitor of metalloproteinase-1,which specifically affects astrocytes involved in glial scar development,increased more than all other cytokines tested,reaching 26.9-fold higher than uninjured rats.After a mild injury,11 cytokines demonstrated sexual dimorphisms;however,after a severe contusion only leptin levels were different between female and male rats.In conclusion,pro-inflammatory cytokines initiate the inflammatory process and return to baseline within hours post-injury,chemokines continue to recruit immune cells for days post-injury,while anti-inflammatory cytokines are downregulated by a week post-injury,and sexual dimorphisms observed after mild injury subsided with more severe injuries.Results from this work define critical chemokines that influence immune cell infiltration and important cytokines involved in glial scar development after spinal cord injury,which are essential for researchers developing treatments targeting secondary damage after spinal cord injury.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.