Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Seismic response of a mid-story isolated structure considering SSI in mountainous areas under long-period earthquakes

At present,there is not much research on mid-story isolated structures in mountainous areas.In this study,a model of a mid-story isolated structure considering soil-structure interaction(SSI)in mountainous areas is established along with a model that does not consider SSI.Eight long-period earthquake waves and two ordinary earthquake waves are selected as inputs for the dynamic time history analysis of the structure.The results show that the seismic response of a mid-story isolated structure considering SSI in mountainous areas can be amplified when compared with a structure that does not consider SSI.The structure response under long-period earthquakes is larger than that of ordinary earthquakes.The structure response under far-field harmonic-like earthquakes is larger than that of near-fault pulse-type earthquakes.The structure response under near-fault pulse-type earthquakes is larger than that of far-field non-harmonic earthquakes.When subjected to long-period earthquakes,the displacement of the isolated bearings exceeded the limit value,which led to instability and overturning of the structure.The structure with dampers in the isolated story could adequately control the nonlinear response of the structure,effectively reduce the displacement of the isolated bearings,and provide a convenient,efficient and economic method not only for new construction but also to retrofit existing structures.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

The environmental analysis and site selection of mussel and large yellow croaker aquaculture areas based on high resolution remote sensing

Mussel aquaculture and large yellow croaker aquaculture areas and their environmental characteristics in Zhoushan were analyzed using satellite data and in-situ surveys.A new two-step remote sensing method was proposed and applied to determine the basic environmental characteristics of the best mussel and large yellow croaker aquaculture areas.This methodology includes the first step of extraction of the location distribution and the second step of the extraction of internal environmental factors.The fishery ranching index(FRI1,FRI2)was established to extract the mussel and the large yellow croaker aquaculture area in Zhoushan,using Gaofen-1(GF-1)and Gaofen-6(GF-6)satellite data with a special resolution of 2 m.In the second step,the environmental factors such as sea surface temperature(SST),chlorophyll a(Chl-a)concentration,current and tide,suspended sediment concentration(SSC)in mussel aquaculture area and large yellow croaker aquaculture area were extracted and analyzed in detail.The results show the following three points.(1)For the extraction of the mussel aquaculture area,FRI1 and FRI2 are complementary,and the combination of FRI1 and FRI2 is suitable to extract the mussel aquaculture area.As for the large yellow croaker aquaculture area extraction,FRI2 is suitable.(2)Mussel aquaculture and the large yellow croaker aquaculture area in Zhoushan are mainly located on the side near the islands that are away from the eastern open waters.The water environment factor template suitable for mussel and large yellow croaker aquaculture was determined.(3)This two-step remote sensing method can be used for the preliminary screening of potential site selection for the mussels and large yellow croaker aquaculture area in the future.the fishery ranching index(FRI1,FRI2)in this paper can be applied to extract the mussel and large yellow croaker aquaculture areas in coastal waters around the world.

Prevalence of Malnutrition and Anaemia in Children ≤ 5 Years of Age in Some Conflict Hit Areas of Meme Division of the South West Region of Cameroon

Malnutrition refers to the deficiency, imbalances, or excesses in a person’s intake of energy or nutrients [1]. Khan defines anaemia as below level of Haemoglobin in red blood shown by a lower number of functioning red blood cells [2]. The crisis in the North West and South West Regions of Cameroon has led to several negative effects on children’s living conditions. There has been an increase in malnutrition and anaemia in the South West Region and Kumba in particular. The main objective of this study was “to examine the prevalence of malnutrition and anaemia in children ≤ 5 years of age in some conflict-hit areas of Meme Division”. A descriptive cross-sectional study was conducted in 2023 from March to June. We recruited 200 children ≤ 5 years into the study from three hospitals. The regional hospital annex in Kumba, Presbyterian General Hospital Kumba and the Ntam Hospital in Kumba. Socio-demographic factors were assessed using questionnaire, nutritional status was assessed by the use anthropometric measurements and an auto haematology analyser was used to determine anaemia. The overall prevalence of malnutrition in the study area was 40.5%. The prevalence of malnutrition varied significantly (P < 0.001) with the study sites. The overall prevalence of anaemia in the study area was 70.5%. The prevalence of anaemia was not significantly associated with the study sites. The prevalence of Malnutrition and Anaemia in children ≤ 5 years of age is very high in the Kumba municipalities. This could be attributed to the ongoing crisis which has caused a lot of social migrations from rural areas to Urban areas which are safer.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Exploring well-being disparities between urban and rural areas:A case study in the Stavropol Territory,Russia

Rural areas are crucial for a country’s sustainable economy.New strategies are needed to develop rural areas to improve the well-being of rural population and generate new job opportunities.This is especially important in countries where agricultural production accounts for a significant share of the gross product,such as Russia.In this study,we identified the key indicators of satisfaction and differences between rural and urban citizens based on their social,economic,and environmental backgrounds,and determined whether there are well-being disparities between rural and urban areas in the Stavropol Territory,Russia.We collected primary data through a survey based on the European Social Survey framework to investigate the potential differences between rural and urban areas.By computing the regional well-being index using principal component analysis,we found that there was no statistically significant difference in well-being between rural and urban areas.Results of key indicators showed that rural residents felt psychologically more comfortable and safer,assessed their family relationships better,and adhered more to traditions and customs.However,urban residents showed better economic and social conditions(e.g.,infrastructures,medical care,education,and Internet access).The results of this study imply that we can better understand the local needs,advantages,and unique qualities,thereby gaining insight into the effectiveness of government programs.Policy-makers and local authorities can consider targeted interventions based on the findings of this study and strive to enhance the well-being of both urban and rural residents.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Quantifying the impact of earthquakes and geological factors on spatial heterogeneity of debris-flow prone areas:A case study in the Hengduan Mountains

Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significance of this knowledge, a comprehensive quantification of the influence of regional topographical and geological factors on the spatial heterogeneity of debris-flow-prone areas has been lacking. This study selected the Hengduan Mountains, an earthquake-prone region characterized by diverse surface conditions and complex landforms, as a representative study area. An improved units zoning and objective factors identification methodology was employed in earthquake and fault analysis to assess the impact of seismic activity and geological factors on spatial heterogeneity of debrisflow prone areas. Results showed that the application of GIS technology with hydrodynamic intensity and geographical units analysis can effectively analyze debris-flow prone areas. Meanwhile, earthquake and fault zones obviously increase the density of debrisflow prone catchments and make them unevenly distributed. The number of debris-flow prone areas shows a nonlinear variation with the gradual increase of geomorphic factor value. Specifically, the area with 1000 m-2500 m elevation difference, 25°-30° average slope, and 0.13-0.15 land use index is the most favorable conditions for debris-flow occurrence;The average annual rainfall from 600 to 1150 mm and landslides gradient from 16° to 35° are the main causal factors to trigger debris flow. Our study sheds light on the quantification of spatial heterogeneity in debris flow-prone areas in earthquake-prone regions, which can offer crucial support for post-debris flow risk management strategies.

Factorized Smith Method for A Class of High-Ranked Large-ScaleТ-Stein Equations

We introduce a factorized Smith method(FSM)for solving large-scale highranked J-Stein equations within the banded-plus-low-rank structure framework.To effectively reduce both computational complexity and storage requirements,we develop techniques including deflation and shift,partial truncation and compression,as well as redesign the residual computation and termination condition.Numerical examples demonstrate that the FSM outperforms the Smith method implemented with a hierarchical HODLR structured toolkit in terms of CPU time.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Ecological-Based Mining:A Coal-Water-Thermal Collaborative Paradigm in Ecologically Fragile Areas in Western China

A substantial reduction in groundwater level,exacerbated by coal mining activities,is intensifying water scarcity in western China’s ecologically fragile coal mining areas.China’s national strategic goal of achieving a carbon peak and carbon neutrality has made eco-friendly mining that prioritizes the protection and efficient use of water resources essential.Based on the resource characteristics of mine water and heat hazards,an intensive coal-water-thermal collaborative co-mining paradigm for the duration of the mining process is proposed.An integrated system for the production,supply,and storage of mining companion resources is achieved through technologies such as roof water inrush prevention and control,hydrothermal quality improvement,and deep-injection geological storage.An active preventive and control system achieved by adjusting the mining technology and a passive system centered on multiobjective drainage and grouting treatment are suggested,in accordance with the original geological characteristics and dynamic process of water inrush.By implementing advanced multi-objective drainage,specifically designed to address the“skylight-type”water inrush mode in the Yulin mining area of Shaanxi Province,a substantial reduction of 50%in water drillings and inflow was achieved,leading to stabilized water conditions that effectively ensure subsequent safe coal mining.An integrated-energy complementary model that incorporates the clean production concept of heat utilization is also proposed.The findings indicate a potential saving of 8419 t of standard coal by using water and air heat as an alternative heating source for the Xiaojihan coalmine,resulting in an impressive energy conservation of 50.2%and a notable 24.2%reduction in carbon emissions.The ultra-deep sustained water injection of 100 m^(3)·h^(-1)in a single well would not rupture the formation or cause water leakage,and 7.87×10^(5)t of mine water could be effectively stored in the Liujiagou Formation,presenting a viable method for mine-water management in the Ordos Basin and providing insights for green and low-carbon mining.

Study on green power supply modes for heavy load in Remote Areas

In this study,the present situation and characteristics of power supply in remote areas are summarized.By studying the cases of power supply projects in remote areas,the experience is analyzed and described,and the applicability of related technologies,such as grid-forming storage and power load management,is studied,including grid-connection technologies,such as grid-forming converters and power load management.On this basis,three power-supply modes were proposed.The application scenarios and advantages of the three modes were compared and analyzed.Based on the local development situation,the temporal sequences of the three schemes are described,and a case study was conducted.The study of the heavy-load power supply mode in remote areas contributes to solving the problem of heavy-load green power consumption in remote areas and promoting the further development of renewable energy.

Coordination Mechanism and Empirical Research on Marine Science Technology Innovation and Marine Eco-civilization:A Case Study of Coastal Areas of China

Marine science technology innovation provides power and guarantees for marine eco-civilization construction,which provides direction and material support for marine science technology innovation.Therefore,the coordinated development of the two is of great significance to the marine economy sustainable development in China’s coastal areas.On the basis of clarifying the connotations of marine science technology innovation and marine eco-civilization in China’s coastal areas from 2006 to 2019,the mechanism for their coordinated development was analysed.A comprehensive indicator system based on the connotation of the two was constructed,and the coordinated development relationship was empirically tested using the coupled coordination model and the panel vector autoregressive(PVAR)model.The results show that:1)the level of China’s coastal marine science technology innovation continues to improve,gradually forming the core of the development of marine science technology innovation in the north,east and south of Shandong,Shanghai and Guangdong;the level of marine eco-civilization development fluctuating upward trend,showing obvious spatial differentiation characteristics.2)The degree of coordination of marine science technology innovation and marine eco-civilization is growing over time.There is no causal relationship between marine science technology innovation and marine eco-civilization in the northern marine economic circle,but there is a two-way causal relationship between the two in the eastern and southern marine economic circles.3)Marine eco-civilization shows a significant positive and continuous impact on marine science technology innovation,and marine science technology innovation shows a long-term,continuous,fluctuating,and lagging impact on marine eco-civilization.The overall role of marine eco-civilization on marine science technology innovation is dominant,and there are significant differences in the impact effects of the two major marine economic circles.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Evolution and Determinants of Population Agglomeration in Less Developed Metropolitan Areas:A Case Study of the Taiyuan Metropolitan Area,China

It is of importance to enhance the urban areas'capacity for population aggregation in underdeveloped regions,aiming to rectify the imbalanced and insufficient pattern of economic development in China.Taking the Taiyuan Metropolitan Area(TMA)in central China as a case study,this paper examines the evolutionary process and characteristics of population agglomeration from 2000 to 2020,and identifies factors associated with agglomeration and their spatial effects.The findings indicated that:1)against the background of sustained population shrinkage in the provincial area,the TMA showed a demographic trend of steady increase,albeit with a decelerated growth rate.In the metropolitan area,urban population size continued to grow rapidly,whereas the rural areas endured sustained losses.Disparities in city size continued to widen,and the polarization of concentrated population in the core cities kept increasing.2)Agglomerations in both secondary and service industries had significant positive effects on local population agglomeration,with the former effect being stronger.Regional economic development,government fiscal expenditure,and financial advancement all contributed to facilitating local population clustering.From a spatial spillover perspective,service agglomeration and financial development promoted population agglomeration in surrounding areas.Conversely,fiscal expenditure inhibited such agglomeration.As for industrial agglomeration and regional economic development,their spatial spillover effects were non-significant.The results obtained reveal several policy implications aimed at enhancing the population agglomeration capacity of the metropolitan area in underdeveloped regions during the new era.

Clinical efficacy of intradermal type Ⅰ collagen injections in treating skin photoaging in patients from high-altitude areas

BACKGROUND Photoaging,a result of chronic sun exposure,leads to skin damage and pigmentation changes.Traditional treatments may have limitations in high-altitude areas like Yunnan Province.Intradermal Col Ⅰ injections stimulate collagen production,potentially improving skin quality.This study aims to assess the efficacy and safety of this treatment for photoaging.AIM To evaluate the efficacy and safety of intradermal typeΙcollagen(ColΙ)injection for treating photoaging.METHODS This prospective,self-controlled study investigated the impact of intradermal injections of ColΙon skin photodamage in 20 patients from the Yunnan Province.Total six treatment sessions were conducted every 4 wk±3 d.Before and after each treatment,facial skin characteristics were quantified using a VISIA skin detector.Skin thickness data were assessed using the ultrasound probes of the Dermalab skin detector.The Face-Q scale was used for subjective evaluation of the treatment effect by the patients.RESULTS The skin thickness of the right cheek consistently increased after each treatment session compared with baseline.The skin thickness of the left cheek significantly increased after the third through sixth treatment sessions compared with baseline.The skin thickness of the right zygomatic region increased after the second to sixth treatment sessions,whereas that of the left zygomatic region showed a significant increase after the fourth through sixth treatment sessions.The skin thickness of both temporal regions significantly increased after the fifth and sixth treatment sessions compared with baseline(P<0.05).These findings were also supported by skin ultrasound images.The feature count for the red areas and wrinkle feature count decreased following the treatment(P<0.05).VISIA assessments also revealed a decrease in the red areas after treatment.The Face-QSatisfaction with Facial Appearance Overall and Face-Q-Satisfaction with Skin scores significantly increased after each treatment session.The overall appearance of the patients improved after treatment.CONCLUSION Intradermal ColΙinjection improves photoaging,with higher patient satisfaction and fewer adverse reactions,and could be an effective treatment method for populations residing in high-altitude areas.