Flexible nest site selection of the endangered Oriental Storks(Ciconia boyciana):Trade-off from adaptive strategies

Nest site selection is a vital component of bird reproduction success,and an adaptive behavior conducted to decrease nest predation risk with avoiding external disturbances.Understanding patterns of nest site selection can provide insights into how species adapt to changes in their habitat and has important conservation implications.In this study,we used microhabitat variables and multi-scale data with a field survey of nest occurrence to determine nest site selection patterns and adaptive strategies of the breeding Oriental Storks(Ciconia boyciana)in different nest areas.Results demonstrate that the nest site microhabitat characteristics of the breeding Oriental Storks significantly differed among the three nesting areas,and nest height was higher in the middle and lower Yangtze River floodplain than in the Northeast China and Bohai Bay nest areas.The food resources and intensity of human disturbance had the greatest effects on the nest site selection of the breeding Oriental Storks.The intensity of human disturbance was positively correlated with the nest height of the breeding Oriental Storks in Bohai Bay and the middle and lower Yangtze River floodplain;however,nest height decreased with the abundance of food resources in the Northeast China nest area.Our findings indicate that the nest site selection patterns of Oriental Storks showed flexible adaptive strategies.In safer environments,nests were lower and closer to food resources,which allows parent storks to invest more in the nestlings.However,in areas where human activity was intense,nests were higher to ensure the safety of their offspring.Some measures that could be taken to improve the breeding habitat of Oriental Storks include increasing the percentage of wetland areas in nesting areas to enhance food resources availability and setting artificial nests at suitable heights in potential nesting grounds to encourage nesting.Finally,the establishment of soft barriers around the nesting areas could increase the safety of nests.

Unveiling the adaptation strategies of woody plants in remnant forest patches to spatiotemporal urban expansion through leaf trait networks

Background:With the expansion of urban areas,the remnants of forested areas play a crucial role in preserving biodiversity in urban environments.This study aimed to explore the impact of spatiotemporal urban expansion on the networks of leaf traits in woody plants within remnant forest patches,thereby enhancing our understanding of plant adaptive strategies and contributing to the conservation of urban biodiversity.Methods:Our study examined woody plants within 120 sample plots across 15 remnant forest patches in Guiyang,China.We constructed leaf trait networks (LTNs) based on 26 anatomical,structural,and compositional leaf traits and assessed the effects of the spatiotemporal dynamics of urban expansion on these LTNs.Results and conclusions:Our results indicate that shrubs within these patches have greater average path lengths and diameters than trees.With increasing urban expansion intensity,we observed a rise in the edge density of the LTN-shrubs.Additionally,modularity within the networks of shrubs decreased as road density and urban expansion intensity increased,and increases in the average path length and average clustering coefficient for shrubs were observed with a rise in the composite terrain complexity index.Notably,patches subjected to‘leapfrog’expansion exhibited greater average patch length and diameter than those experiencing edge growth.Stomatal traits were found to have high degree centrality within these networks,signifying their substantial contribution to multiple functions.In urban remnant forests,shrubs bolster their resilience to variable environmental pressures by augmenting the complexity of their leaf trait networks.

Engineering Strategies for Suppressing the Shuttle Effect in Lithium–Sulfur Batteries

Lithium–sulfur(Li–S)batteries are supposed to be one of the most potential next-generation batteries owing to their high theoretical capacity and low cost.Nevertheless,the shuttle effect of firm multi-step two-electron reaction between sulfur and lithium in liquid electrolyte makes the capacity much smaller than the theoretical value.Many methods were proposed for inhibiting the shuttle effect of polysulfide,improving corresponding redox kinetics and enhancing the integral performance of Li–S batteries.Here,we will comprehensively and systematically summarize the strategies for inhibiting the shuttle effect from all components of Li–S batteries.First,the electrochemical principles/mechanism and origin of the shuttle effect are described in detail.Moreover,the efficient strategies,including boosting the sulfur conversion rate of sulfur,confining sulfur or lithium polysulfides(LPS)within cathode host,confining LPS in the shield layer,and preventing LPS from contacting the anode,will be discussed to suppress the shuttle effect.Then,recent advances in inhibition of shuttle effect in cathode,electrolyte,separator,and anode with the aforementioned strategies have been summarized to direct the further design of efficient materials for Li–S batteries.Finally,we present prospects for inhibition of the LPS shuttle and potential development directions in Li–S batteries.

How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

A systematic review of climate change impacts,adaptation strategies,and policy development in West Africa

Climate change studies are diverse with no single study giving a comprehensive review of climate change impacts,adaptation strategies,and policy development in West Africa.The unavailability of an all-inclusive study to serve as a guide for practitioners affects the effectiveness of climate change adaptation strategies proposed and adopted in the West African sub-region.The purpose of this study was to review the impacts of climate change risks on the crop,fishery,and livestock sectors,as well as the climate change adaptation strategies and climate-related policies aimed at helping to build resilient agricultural production systems in West Africa.The review process followed a series of rigorous stages until the final selection of 56 articles published from 2009 to 2023.Generally,the results highlighted the adverse effects of climate change risks on food security.We found a continuous decline in food crop production.Additionally,the livestock sector experienced morbidity and mortality,as well as reduction in meat and milk production.The fishery sector recorded loss of fingerlings,reduction in fish stocks,and destruction of mariculture and aquaculture.In West Africa,climate-smart agriculture technologies,physical protection of fishing,and inclusion of gender perspectives in programs appear to be the major adaptation strategies.The study therefore recommends the inclusion of ecosystem and biodiversity restoration,weather insurance,replacement of unsafe vessels,and strengthening gender equality in all climate change mitigation programs,as these will help to secure enough food for present and future generations.

Drug resistance mechanisms in cancers:Execution of prosurvival strategies

One of the quintessential challenges in cancer treatment is drug resistance.Several mechanisms of drug resistance have been described to date,and new modes of drug resistance continue to be discovered.The phenomenon of cancer drug resistance is now widespread,with approximately 90% of cancer-related deaths associated with drug resistance.Despite significant advances in the drug discovery process,the emergence of innate and acquired mechanisms of drug resistance has impeded the progress in cancer therapy.Therefore,understanding the mechanisms of drug resistance and the various pathways involved is integral to treatment modalities.In the present review,I discuss the different mechanisms of drug resistance in cancer cells,including DNA damage repair,epithelial to mesenchymal transition,inhibition of cell death,alteration of drug targets,inactivation of drugs,deregulation of cellular energetics,immune evasion,tumor-promoting inflammation,genome instability,and other contributing epigenetic factors.Furthermore,I highlight available treatment options and conclude with future directions.

Farmers’ Knowledge and Perceptions of the Effects of Climate Variability and Pollution on Crop Production and Their Varying Adaptation Strategies in The Gambia

Crop and livestock production is critical to food security in The Gambia. Over the years, the country has experienced a reduced yield due to perceived climate change events with limited studies on how climate change and pollution affect crop production. This study assesses farmers’ knowledge and perceptions of the effects of climate variability and pollution on crop production and their varying adaptation strategies in The Gambia. Both quantitative and qualitative methods were used in this study. The sample size for quantitative data collection was calculated as 432 while the qualitative data involves both the focus group discussions and key informant interviews. The focus group discussions comprised two districts in each of the six agricultural regions and two farming communities engaged in crop production were chosen from each district. Furthermore, eight key informant interviews from relevant institutions were conducted. The study shows that The Gambia is highly vulnerable to extreme climatic events. Although most farmers opined that agricultural land contamination emanates from farm runoff and indiscriminate waste dumping, they had little knowledge of heavy metal pollution and bioremediation. The results showed that farmers experienced constraints such as inadequate access to credit, water, and irrigation facilities, insufficient access to efficient inputs, salt intrusion, etc. which threatened food security. The study concludes that crop farmers acknowledged the existence and impacts of climate change, and therefore recommend the availability and affordability of climate change resilient crops and promote variability awareness campaigns to address climate change impacts in The Gambia.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Overview of the immunological mechanisms in hepatitis B virus reactivation:Implications for disease progression and management strategies

Hepatitis B virus(HBV)reactivation is a clinically significant challenge in disease management.This review explores the immunological mechanisms underlying HBV reactivation,emphasizing disease progression and management.It delves into host immune responses and reactivation’s delicate balance,spanning innate and adaptive immunity.Viral factors’disruption of this balance,as are interac-tions between viral antigens,immune cells,cytokine networks,and immune checkpoint pathways,are examined.Notably,the roles of T cells,natural killer cells,and antigen-presenting cells are discussed,highlighting their influence on disease progression.HBV reactivation’s impact on disease severity,hepatic flares,liver fibrosis progression,and hepatocellular carcinoma is detailed.Management strategies,including anti-viral and immunomodulatory approaches,are critically analyzed.The role of prophylactic anti-viral therapy during immunosuppressive treatments is explored alongside novel immunotherapeutic interventions to restore immune control and prevent reactivation.In conclusion,this compre-hensive review furnishes a holistic view of the immunological mechanisms that propel HBV reactivation.With a dedicated focus on understanding its implic-ations for disease progression and the prospects of efficient management stra-tegies,this article contributes significantly to the knowledge base.The more profound insights into the intricate interactions between viral elements and the immune system will inform evidence-based approaches,ultimately enhancing disease management and elevating patient outcomes.The dynamic landscape of management strategies is critically scrutinized,spanning anti-viral and immunomodulatory approaches.The role of prophylactic anti-viral therapy in preventing reactivation during immunosuppressive treatments and the potential of innovative immunotherapeutic interventions to restore immune control and proactively deter reactivation.

Problem identification and revitalization strategies for the recovery and reconstruction of traditional villages in the Ms 6.8 Luding Earthquake

Post-disaster reconstruction is a topic of global concern,and traditional villages have special heritage attributes and need to face more requirements and obstacles in post-disaster reconstruction.This paper summarizes four concepts based on the research on post-disaster reconstruction both domestically and internationally,as well as the recovery and reconstruction of cultural heritage.Through a field survey of traditional villages in the Ms 6.8 Luding earthquake-stricken area,it is found that there are problems such as insufficient awareness of heritage value,misalignment of scientific reconstruction technology,and insufficient protection of reconstruction elements during the reconstruction process.Traditional villages face the risk of declining or even loss of heritage value.In order to effectively protect traditional villages and inherit the carrier of regional culture,four targeted reconstruction response strategies are proposed,i.e.,to"establish special planning for traditional village preservation","emphasize recovery of the authenticity of village heritage","ensure elements for village heritage recovery"and"promote the activation and utilization of village heritage",based on the problems discovered during the survey and the four concepts summarized in the research on post-disaster reconstruction of traditional villages.The research results hope to provide useful reference for ancient cultural areas affected by earthquakes on how to protect cultural heritage during the post-disaster reconstruction process.

Progress on Transition Metal Ions Dissolution Suppression Strategies in Prussian Blue Analogs for Aqueous Sodium-/Potassium-Ion Batteries

Aqueous sodium-ion batteries(ASIBs)and aqueous potassium-ion batteries(APIBs)present significant potential for large-scale energy storage due to their cost-effectiveness,safety,and environmental compatibility.Nonetheless,the intricate energy storage mechanisms in aqueous electrolytes place stringent require-ments on the host materials.Prussian blue analogs(PBAs),with their open three-dimensional framework and facile synthesis,stand out as leading candidates for aqueous energy storage.However,PBAs possess a swift capacity fade and limited cycle longevity,for their structural integrity is compromised by the pronounced dis-solution of transition metal(TM)ions in the aqueous milieu.This manuscript provides an exhaustive review of the recent advancements concerning PBAs in ASIBs and APIBs.The dissolution mechanisms of TM ions in PBAs,informed by their structural attributes and redox processes,are thoroughly examined.Moreover,this study delves into innovative design tactics to alleviate the dissolution issue of TM ions.In conclusion,the paper consolidates various strategies for suppressing the dissolution of TM ions in PBAs and posits avenues for prospective exploration of high-safety aqueous sodium-/potassium-ion batteries.

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

LDAS&ET-AD:Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation

Adversarial distillation(AD)has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training.However,fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation.Additionally,the reliability of guidance from static teachers diminishes as target models become more robust.This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation(LDAS&ET-AD).Firstly,a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation.A strategy model is introduced to produce attack strategies that enable adversarial examples(AEs)to be created in areas where the target model significantly diverges from the teachers by competing with the target model in minimizing or maximizing the AD loss.Secondly,a teacher evolution strategy is introduced to enhance the reliability and effectiveness of knowledge in improving the generalization performance of the target model.By calculating the experimentally updated target model’s validation performance on both clean samples and AEs,the impact of distillation from each training sample and AE on the target model’s generalization and robustness abilities is assessed to serve as feedback to fine-tune standard and robust teachers accordingly.Experiments evaluate the performance of LDAS&ET-AD against different adversarial attacks on the CIFAR-10 and CIFAR-100 datasets.The experimental results demonstrate that the proposed method achieves a robust precision of 45.39%and 42.63%against AutoAttack(AA)on the CIFAR-10 dataset for ResNet-18 and MobileNet-V2,respectively,marking an improvement of 2.31%and 3.49%over the baseline method.In comparison to state-of-the-art adversarial defense techniques,our method surpasses Introspective Adversarial Distillation,the top-performing method in terms of robustness under AA attack for the CIFAR-10 dataset,with enhancements of 1.40%and 1.43%for ResNet-18 and MobileNet-V2,respectively.These findings demonstrate the effectiveness of our proposed method in enhancing the robustness of deep learning networks(DNNs)against prevalent adversarial attacks when compared to other competing methods.In conclusion,LDAS&ET-AD provides reliable and informative soft labels to one of the most promising defense methods,AT,alleviating the limitations of untrusted teachers and unsuitable AEs in existing AD techniques.We hope this paper promotes the development of DNNs in real-world trust-sensitive fields and helps ensure a more secure and dependable future for artificial intelligence systems.

An Adaptive Program Recommendation System for Multi-User Sharing Environment

More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and they cannot make effective use of the mixed information generated by multi-user when exploring users’potential interests.To solve these problems,this paper proposes an adaptive program recommendation system for multi-user sharing environment.Specifically,we first design an offline periodic identification module by building multi-user features and periodically predicting target user in future sessions,which can separate the profile of target user from mixed log records.Subsequently,an online recommendation module with adaptive timevarying exploration strategy is constructed by jointly using personal information and multi-user social information provided by identification module.On one hand,to learn the dynamic changes in user-interest,a time-varying linear upper confidence bound(LinUCB)based on personal information is designed.On the other hand,to reduce the risk of exploration,a timeinvariant LinUCB based on separated multi-user social information from one account/device is proposed to compute the quality scores of programs for each user,which is integrated into the time-varying LinUCB by cross-weighting strategy.Finally,experimental results validate the efficiency of the proposed scheme.

Eradication Treatment of Helicobacter pylori Infection: Evaluation of Therapeutic Strategies in N’Djamena

Background: Helicobacter pylori (Hp) infection is the most widespread bacterial infection in the world. The infection is generally acquired in childhood, but can persist into adulthood. Eradication therapy has undergone several modifications. The aim of this study was to evaluate the different therapeutic strategies used in the eradication of Helicobacter pylori infection in the Centre Hospitalier Universitaire La Reference Nationale of N’Djaména. Patients and Methods: This was a prospective, descriptive analytical study spread over one year, from September 2021 to September 2022. Patients at least 15 years of age presenting with dyspeptic symptoms, seen consecutively in a hepato-gastroenterology consultation and with a positive stool test for H. pylori infection, were included in the study. Equally, 1/3 of patients were treated with dual or triple therapy. The remaining third received quadritherapy. Results: A total of 268 patients were included in the study (mean age 38.40 ± 14.66 with extremes of 16 and 80 years). Males predominated in 58% of cases. Overall therapeutic efficacy was 88.9%. According to different therapeutic strategies, efficacy was 90.75% for dual therapy with PPI (Rabeprazole) and Amoxicillin. On the other hand, efficacy was 87% and 88.88% for PPI-based triple therapy and dual antibiotic therapy, and for PPI-based quadruple therapy and triple antibiotic therapy. Conclusion: H. pylori infection is a common disease in Chad. Dual therapy with rabeprazole combined with a high dose of amoxicillin over a period of at least two weeks showed similar if not better efficacy than triple or quadruple therapy.

Solar adaptive optics systems for the New Vacuum Solar Telescope at the Fuxian Lake Solar Observatory

Adaptive optics(AO)is essential for high-quality ground-based observations with large telescopes because it counters the impact of wavefront aberrations caused by atmospheric turbulence.The new vacuum solar telescope(NVST)is one of the most important high-resolution solar observation instruments in the world.Three sets of solar adaptive optics systems have been developed and installed on this telescope:conventional adaptive optics,ground layer adaptive optics,and multi-conjugate adaptive optics.These have been in operation from 2018 to 2023.This paper details the development and application of solar adaptive optics on the NVST and discusses the newest instrumentation.