Technique For Order Preference By Similarity To Ideal Solution Research Articles

Using the TOPSIS method to select the best low-toxicity organic cosolvent for rice-based toxicity tests.

The application of low-toxicity cosolvents in phytotoxicity tests is a common technique to enhance the distribution of non-water-soluble organic pollutants in the aqueous phase. In this study, the physiological and biochemical responses of rice seedlings to four commonly used organic solvents i.e., dimethyl sulfoxide (DMSO), N,N-dimethylformamide (DMF), pyridine, and Tween-80 were investigated using 16 parameters to assess their impacts on rice plants. Results from phytotoxicity tests indicated that these organic solvents caused varying toxic responses in rice plants and significantly affected the uptake and distribution of mineral nutrients within rice tissues. In order to select the best organic solvent for rice-based toxicity tests among the four solvents with optimal concentrations, the technique for order preference by similarity to ideal solution (TOPSIS) was employed to minimize the variations across different treatment groups. Three different weighting coefficients, namely entropy weight coefficients (EWCs), contribution factor coefficients (CFCs), and combination effective coefficients (CECs) were adopted to calibrate the initially created evaluation matrices. All estimations were judged by the relative closeness coefficients, in which the higher relative closeness value means the better solution to the optimal result. Among the four organic solvents evaluated by TOPSIS, pyridine at 1.24 mmol/L emerged as the most effective solution for rice-based phytotoxicity tests. Overall, this study represents the first attempt to comprehensively evaluate the toxic responses of cosolvents to rice plants using the TOPSIS method. It also offers valuable insights into a number of ecotoxicological applications like the analysis of solvent-generated extracts of environmental matrices from soils and sediments, or the detection of organic compounds induced toxicity with cosolvents.

Sistem Pendukung Keputusan Menentukan Pemilihan Lokasi untuk Cabang Baru Toko Liv Beauty Cosmetic menggunakan Metode TOPSIS

Toko Liv Beauty is one of the business players in the beauty sector that is developing in North Sumatra, specifically in the West Binjai sub-district, Binjai City. As a store that provides various beauty products, this research aims to assist Toko Liv Beauty in determining a strategic location for opening a new branch using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The TOPSIS method was chosen for its ability to analyze alternatives based on positive and negative ideal solutions objectively. A case study was conducted at three potential locations in Binjai: Binjai City, Binjai South, and Binjai North, considering five main criteria: population density, ease of transportation access, number of competitors, rental costs, and building area. The analysis process involves normalizing the decision matrix, calculating weighted values, identifying ideal solutions, and determining alternative preferences. The analysis results show that the location with the highest preference is Binjai North (1), followed by Binjai South (0.5885) and Binjai City (0). Thus, Binjai North is recommended as a strategic location for opening a new branch of Toko Liv Beauty. The implementation of the TOPSIS method in this research is expected to contribute to more effective data-driven decision-making for the business development of Toko Liv Beauty.

The intersection of finTech adoption, HR competency potential, service innovation, and firm growth in the banking sectors using Entropy and TOPSIS.

The adoption of Financial Technology (FinTech), along with the enhancement of Human Resource (HR) competencies, service innovation, and firm growth, plays a crucial role in the development of the banking sector. Despite their importance, obtaining reliable results is often challenging due to the complex, high-dimensional correlations among various features that affect the industry. To address this issue, this research introduces a hybrid Multi-Criteria Decision-Making (MCDM) model that integrates the Entropy-Weighted Method (EWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The primary objective of this study is to systematically evaluate and rank multiple alternatives based on key criteria using the EWM-TOPSIS approaches. Specifically, the analysis considers eleven multifaceted characteristics and eight potential alternatives (A1 to A8), revealing the significant influence of the proposed MCDM approaches in assessing FinTech adoption, HR competency, service innovation, and firm growth. The findings underscore the effectiveness of the entropy-TOPSIS approaches in providing a structured analysis for a smarter and well-informed decision-making. Ultimately, this research proposes the best alternative from the evaluated options, contributing valuable insights into the future role of FinTech, HR competencies, service innovation, and firm growth within the banking sector.

Risk Analysis of Digital Twin Project Operation Based on Improved FMEA Method

With the advent of digitization, digital twin technology is gradually becoming one of the core technologies of the Industry 4.0 era, highlighting the increasing importance of digital twin project management. Despite its potential, DT projects face significant risks during implementation, stemming from technical, managerial, and operational complexities. To address these challenges, this study proposes an improved failure mode and effect analysis (FMEA) framework by integrating double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This framework converts qualitative assessments into quantitative metrics and calculates weights using a hybrid approach, enabling more precise risk prioritisation. Application of the model to an automotive manufacturing company’s DT project identified key risks, particularly in the iteration and upgrade phase, emphasising the importance of cross-departmental collaboration and robust digital infrastructure. The proposed model provides a systematic framework for enterprises to assess and mitigate risks, ensuring the successful deployment of DT projects.

Financial performance evaluation of firms in BIST 100 index with ITARA and COBRA methods

Evaluating firms’ financial performance is important for survival in the competitive environment arising from technological advancements and gaining a competitive advantage. This study aims to assess the financial performances of firms traded on the Istanbul Stock Exchange 100 Index (Borsa Istanbul (BIST) 100 Index) between 2018 and 2022 using an integrated multi-criteria decision-making (MCDM) method. This is the first study to use a new combined approach proposed based on the indifference threshold-based attribute ratio analysis (ITARA) and cost estimation, benchmarking, and risk assessment (COBRA) methods, which have not been applied to corporate performance assessment. The ITARA method is used to find the weights of the criteria, and the ranking of the firms in terms of financial performance is obtained using the COBRA method. The results show that the firms with the highest financial performance are ISMEN, ALGYO, PGSUS, KOZAA, and IPEKE. The companies with the lowest financial performance are AKFGY, BAGFS, MGROS, KOZAL, and GOZDE. The results of this ranking provide important information for firms to recognize their position and for investors who want to invest in firms in the BIST 100 Index. Additionally, sensitivity analyses are performed to assess the impact of changes in the criteria on the ranking of the firms and to validate the results of the proposed method. A comparative analysis is made with different MCDM methods like the technique for order preference by similarity to ideal solution (TOPSIS), and combined compromise solution (CoCoSo) methods and Spearman’s rank correlation test results are presented. This approach leads to the conclusion that the proposed approach can be a useful and effective tool for assessing the financial performance of firms.

Screening and evaluation of key technologies for non-bioartificial liver care: an empirical study.

To identify key technologies within non-bioartificial liver (NBAL, an extracorporeal support system that temporarily replaces some of the liver's functions) nursing to offer guidance for clinical practice. In the context of NBAL nursing, key technologies are crucial for successful implementation of artificial liver treatment, ensuring patient safety, and enhancing nursing quality. A review of both domestic and foreign literature revealed that studies on NBAL nursing technology are lacking and that the key technologies for NBAL nursing have not been clearly identified. Using empirical research methods to collect and analyze data. First, the on-site survey method and literature research method were used to create a preliminary screening list of key technologies for NBAL care. Next, the focus group discussion method was used to establish the screening principles and evaluation indicators for these key technologies. Then, a two-round Delphi study via e-mail correspondence was used to screen and determine the key technologies for NBAL care. Finally, the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) comprehensive evaluation method were applied to evaluate these key technologies for NBAL care. Seventeen key technologies for NBAL care were identified. These include three basic nursing technologies, seven operating techniques, three items for treatment process monitoring technology, two items for health education, and two items for complication prevention and treatment technology. This study identified key NBAL nursing technologies, offering a systematic guide to enhance clinical practice. These technologies improve treatment safety, efficacy, and nursing standards, laying a foundation for NBAL care advancement.

Trivariate risk assessment to unlock priorities in agriculture-dominated semi-arid region.

This study provides crucial insights into sustainable water resource management in an agriculture-dominated, water-scarce region. The long-term hydrologic potential of the Purna sub-catchment (in India) was simulated using the Soil and Water Assessment Tool (SWAT) under a multimetric calibration approach. A comprehensive evaluation of the SWAT-simulated streamflows, incorporating graphical and quantitative assessments (i.e., R2≥0.82 and NSE≥0.81), parametric sensitivity and uncertainty analyses, revealed the hydrological advantages of the multimetric calibration approach over single-site calibration. We introduce the hydrological efficacy index (HEI), representing the natural resiliency of the watersheds, to identify hydrologically efficient areas using long-term hydrological potential and principal component analysis (PCA). The PCA is used to deal with multicollinearity among hydrological variables. The overall vulnerability of the region to escalating water scarcity is determined using multiple hydrologic and socioeconomic indicators derived from observations, simulations, and census data to establish the vulnerability index (VI). Without watershed-based observations, the simulated water yield at 50% dependability and census-based population data are used to derive the areal water stress index (AWSI). The trivariate risk analysis of HEI-AWSI-VI indicated that 72.4% of the study region exhibits moderate to high AWSI and VI, with only 37.9% displaying high HEI. The top 16 priority-ranked watersheds, identified using the analytic hierarchy process (AHP) and modified technique for order preference by similarity to ideal solution (TOPSIS) methods, would bring substantial benefits under sustainable water management practices, covering 60.8% of the area and 75.6% of the population. This study underscores the critical need for implementing comprehensive water conservation practices and strategic resource management to enhance resilience against escalating water scarcity.

Comparative Analysis of Multicriteria Inventory Classification and Forecasing: A Case Study in PT XYZ

One crucial aspect of supply chain management is inventory management. Inefficient inventory management can lead to various issues, such as product expiration, where a high number of items in the warehouse either have expired or are approaching expiration. This issue is experienced by a distribution SME in Indonesia, PT XYZ. Without such classifications, it becomes challenging to predict demand and manage stock levels efficiently. Therefore, the aim of this study is to classify inventory to identify the most important items to business and make a forecasting model of sales quantity to predict inventory replenishment using machine learning algorithms. To advance our research, we adopted the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For inventory classification, we conducted a hybrid approach that combined TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ABC analysis (A: high-value items, B: medium-value items, and C: low-value items). The data employed in this study comprised secondary data, including purchase orders, sales orders, and stock movement records. The result reveals that 11 of the total 383 items under class A are important items for business. After obtaining labels from the ABC Analysis, we proceed to train models using KNN, SVC, and Random Forest for predicting inventory classification. Notably, the Random Forest model showcased remarkable performance and outperformed the rest of the models, achieving an accuracy of 99.21%. For inventory forecasting ARIMA displays a competitive performance with RMSE value 5.305 and MAE value 3.476, indicating a relatively accurate prediction with lower forecasting errors than two other models

Enhancing technical proposal evaluation in consultant selection in Department of Water Resources and Irrigation, Nepal: a fuzzy analytic hierarchy process and fuzzy TOPSIS

In today's dynamic construction landscape, where challenges like complexity, costs, and uncertainties loom large, the selection of consultants emerges as a pivotal factor for project triumph. Among the array of methods available, Quality and Cost Based Selection (QCBS) stands out for its holistic approach, considering both technical prowess and financial considerations. Yet, amidst its structured framework, the evaluation of technical proposals within QCBS often grapples with subjectivity, despite the presence of well-defined criteria. This study boldly tackles this challenge head-on by delineating common criteria and sub-criteria for consultant selection within the esteemed Department of Water Resources and Irrigation (DWRI). Leveraging the innovative Fuzzy Analytic Hierarchy Process (AHP), our endeavor is to ascertain the relative significance of these criteria and sub-criteria. Through the fusion of Fuzzy AHP and Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS), our study aspires to revolutionize consultant ranking methodologies. The ingenious application of Fuzzy AHP empowers us to derive independent weights for criteria and sub-criteria, thereby embracing the inherent ambiguity and nuances of qualitative factors. Subsequently, employing Fuzzy TOPSIS, we aim to systematically rank consultant alternatives based on their Closeness Coefficients, heralding a paradigm shift in evaluation paradigms. Through this groundbreaking hybrid approach, our study endeavors to elevate the consultant selection process, ensuring an impeccable alignment of chosen consultants with project requisites and benchmarks. By dispelling the shadows of subjective decision-making uncertainties, our research holds the promise of ushering in a new era of excellence in the realm of construction projects under the aegis of the Department of Water Resources and Irrigation.

Screening of Suitable Species Combinations for Vegetation Restoration in Ecologically Damaged Areas of Representative Coal Mines on the South Slope of Tianshan Mountain

Coal mining has significantly degraded the ecological environment, and the ecosystem structure of the southern slopes of the Tianshan Mountains is complex, with drought, low rainfall, and ecological fragility increasing the difficulty of ecological restoration. There are few studies on the selection of suitable pioneer species for the restoration of mining areas on the south slope of Tianshan Mountain. In this study, we investigated the artificial restoration of vegetation in the damaged areas of nine representative coal mines on the south slope of the Tianshan Mountains. We categorized the coal mines into three classes based on elevation. Using a combination of the entropy weight method and the technique for order preference by similarity to ideal solution (TOPSIS), indicators such as the diversity index and vegetation cover were selected to compare the recovery of vegetation in the mining area and screen for suitable pioneer species in ecologically damaged areas. The results show the following: (1) Based on the entropy weight TOPSIS findings, Run Hua coal mine, Da Wanqi coal mine, and Ke Xing coal mine are the highest-scoring coal mines among different types of coal mines. (2) Based on the score results and species rankings, species combinations including Agropyron cristatum, Achnatherum splendens, Medicago sativa Linn, and Halogeton glomeratus are recommended for altitudes below 1500 m. For altitudes between 1500 m and 2000 m, combinations including Festuca elata Keng ex E, Agropyron cristatum, Halogeton glomeratus, and Artemisia sieversiana Ehrhart ex Willd are recommended; similarly, combinations including Agropyron cristatum, Achnatherum splendens, Seriphidium kaschgaricum, and Halogeton glomeratus are recommended for elevations above 2000 m. This study discusses the recovery of artificial vegetation communities within 2 years, with a view to providing a theoretical basis and data support for the recovery of the remaining ecologically damaged areas of the same types.

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN RUMAH TINGGAL MENGGUNAKAN METODE TOPSIS

Choosing a residential property is a complex decision involving multiple criteria such as price, location, facilities, and risks. To assist potential buyers in making more objective decisions, this research developed a Decision Support System (DSS) using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. TOPSIS is employed to process various criteria and rank housing alternatives based on their proximity to the ideal solution. The results demonstrate that this system effectively provides recommendations that align with user preferences, with location and price being the dominant factors. The implementation of TOPSIS is expected to improve user satisfaction and reduce dissatisfaction risks in residential property selection.

A Comprehensive Review of MCDM Methods, Applications, and Emerging Trends

Multiple-criteria decision-making (MCDM) approaches have become vital for tackling complicated, multi-objective decision-making issues in dynamic and unpredictable situations. This paper covers the history of MCDM methodologies, ranging from conventional methods like the analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to sophisticated innovations embracing fuzzy logic, hybrid models, and artificial intelligence (AI). It analyses the numerous uses of MCDM in business, engineering, healthcare, and environmental management, highlighting their adaptation to both qualitative and quantitative criteria. The approaches adopted include an examination of conventional and current frameworks, identifying their strengths and shortcomings. Emerging technologies such as integrating Blockchain, the Internet of Things, and big data analytics are studied, revealing their potential for real-time and dynamic decision-making. Key findings underscore the limitations of uncertainty modeling, computational complexity, and scalability while also revealing potential for multidisciplinary research and sustainability-focused applications. This study finds by offering actionable recommendations for researchers and practitioners, advocating for the establishment of AI-integrated, real-time decision-making frameworks and standardized evaluation standards to address contemporary challenges and enhance the practical relevance of MCDM methods.

Optimization of Thermal and Pressure Drop Performance in Circular Pin Fin Heat Sinks Using the TOPSIS Method

This study aims to optimize the thermal performance of pin fin heat sinks by minimizing the maximum temperature of the heat source. Using ANSYS ICEPAK, simulations were conducted for various design parameters, including the number of fins, inlet flow rate, and fin thickness, across circular fins in both inline and staggered arrangements. The circular staggered configuration with 36 fins (3 mm thick) and a flow rate of 6 CFM (Cubic Feet per Minute) achieved the lowest temperature of 34.96 °C, outperforming the inline arrangement. The Taguchi method helped strike a balance between heat transfer and pressure drop, revealing that flow rate has a greater influence when varied compared to the number of fins and fin thickness. An optimal configuration was identified with 36 fins and a flow rate of 4 CFM, which was less sensitive to operational variations. Analysis of Variance (ANOVA) revealed that inlet flow rate significantly impacts heat sink performance, while polynomial regression models demonstrated strong generalization capabilities, with Root mean square error (RMSE) of 8.92%. These findings provide reliable predictive tools and practical insights for optimizing heat sink designs in electronics cooling applications. By utilizing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, the coefficient of relative closeness (Cn*) is plotted as a main effect. Referring to the multi-objective optimization-based TOPSIS method, it is found that the attributes are partly from the inlet flow rate (Q) are 63.4% of the number of fins (Nf) (25.05%).

Multi-criteria decision making for evaluating a suitable MOO technique to improve the performance of AISI 1040 carbon steel and Ti-6Al-4V alloy

The objective of this work is to compare the machining performance of AISI 1040 Carbon Steel and Ti-6Al-4V alloy for a slot cutting operation using a 3-axis vertical milling machine. A set of 16 experiments was conducted, considering four factors: Feed rate, depth of cut, spindle speed, and coolant concentration, to remove material up to a finite depth. Three multi-objective optimization techniques namely, Grey Relational Analysis (GRA), Multi-Response Signal-to-Noise Ratio (MRSN Ratio), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were employed to determine the optimal machining conditions by maximizing the material removal rate and minimizing surface roughness and material hardness. It has been observed that the response performance index varies with different optimization techniques as well as different workpiece materials. The most significant contributions of each technique were evaluated through ANOVA analysis. A prediction model was developed using linear regression analysis. Finally, comparing the results of the three objective optimization techniques, GRA gives better performance than both TOPSIS and MRSN by effectively satisfying two objectives (i.e., maximizing material removal rate and minimizing surface roughness) for AISI 1040 Carbon Steel. In contrast, the MRSN technique provides better results than GRA and TOPSIS by effectively satisfying two objectives (i.e., maximizing material removal rate and minimizing material hardness) for Ti-6Al-4V alloy.

COMPARATIVE ASSESSMENT OF FINANCIAL PERFORMANCE AMONG AIR NAVIGATION SERVICE PROVIDERS

The significant expansion of the aviation industry highlights the crucial need for financial resilience and strategic governance among stakeholders, particularly emphasizing the essential role of air navigation service providers (ANSPs). The aim of the study was to present a model for the assessment and comparison of the financial performance of seventeen ANSPs. The financial performance of the seventeen ANSPs was evaluated using nine financial ratios, with the combined scores subsequently analyzed using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method over a five-year period (2018-2022). The study revealed that DSNA demonstrated resilience in the face of the crisis, whereas ENAIRE was unable to sustain its previous financial performance among the 17 ANSPs over the past years. It is recommended that further analyses be conducted using a range of criteria, that financial strategies for crisis resilience be investigated, and that global aviation trends across regions be explored.

Participatory Urban Planning for Social Sustainability: A Combination of the Analytic Hierarchy Process, with Strengths, Weaknesses, Opportunities, Threats Analysis, and the Technique for Order Preference by Similarity to Ideal Solution (A’WOT-TOPSIS)

This study explores the role of participation in achieving social sustainability in urban environments. As uncertainties about the future grow, the need for methods that ensure the representation of diverse stakeholders becomes essential. The Participatory A’WOT-TOPSIS Method is introduced as an effective approach for managing multi-actor and multi-decision-making processes. This Multi-Criteria Decision-Making (MCDM) method combines SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis with the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). An empirical application was conducted to assess various urban scenarios through a strategic planning process involving five distinct stakeholder groups. Using an inductive approach, one of three scenarios was selected. Findings demonstrate that the proposed method enhances transparency, ensures objectivity, reduces inconsistencies in stakeholder decision-making, and promotes collaborative representation. However, increasing the number of decision-makers and decisions may lead to greater workload and time demands for those implementing the method. This approach lays the groundwork for future research incorporating elements like representation, belonging, and identity into participatory processes to foster social sustainability in urban areas.

Dynamic ensemble-based machine learning models for predicting pest populations

Early prediction of pest occurrences can enhance crop production, reduce input costs, and minimize environmental damage. Advances in machine learning algorithms facilitate the development of efficient pest alert systems. Furthermore, ensemble algorithms help in the utilization of several models rather than being dependent on a single model. This study introduces a dynamic ensemble model with absolute log error (ALE) and logistic error functions using four machine learning models—artificial neural networks (ANNs), support vector regression (SVR), k-nearest neighbors (kNN), and random forests (RF). Various abiotic factors such as minimum and maximum temperature, rainfall, and morning and evening relative humidity were incorporated into the model as exogenous variables. The proposed algorithms were compared with fixed-weighted and unweighted ensemble methods, and candidate machine learning models, using the pest population data for yellow stem borer (YSB) from two regions of India. Error metrics include the root mean square log error (RMSLE), root relative square error (RRSE), and median absolute error (MDAE), along with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. This study concluded that the proposed dynamic ensemble algorithm demonstrated better predictive accuracy in forecasting YSB infestation in rice crops.

Perancangan Sistem Pendukung Keputusan Penilaian Kinerja Karyawan Terbaik pada PT XYZ menggunakan Metode TOPSIS

PT XYZ is a company in the field of construction, development, and management of construction projects. They are responsible for planning, managing, and implementing construction projects, both for infrastructure and commercial buildings. The previous assessment did not have a weighting of the criteria assessment and took time to process the value of each employee because the assessment still used a manual method (assisted by calculator calculations) so that the employee calculation process was processed one by one. This is not in accordance with the director's expectations and has an impact on the assessment being inaccurate and detrimental to the company in terms of time. Therefore, to evaluate the performance of top employees at PT XYZ, this decision support system application was created, with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method which can produce a simple Decision Support System with an easy-to-understand and logical concept in the PT XYZ Employee Performance Assessment.

Post-harvest processing methods have critical roles on the contents of active metabolites and pharmacological effects of Astragali Radix

BackgroundProcessing methods of traditional Chinese medicinal materials are critical in influencing the active metabolites and pharmacological effects. The fresh processing method effectively prevents the loss and degradation of metabolites, common in traditional drying and softening processes, while also reducing production costs. Astragali Radix (AR), a leguminous botanical drug, is widely utilized in clinical practice and functional foods. Therefore, optimizing its the post-harvest processing method is crucial for enhancing production and application.MethodsAR samples were processed using different methods with varying moisture content, including unpretreated samples and those subjected to kneading and sweating treatments. These samples were evaluated for physical appearance and active metabolite content. The entropy weight method, combined with the technique for order preference by similarity to ideal solution (TOPSIS), was employed to optimize the fresh processing method. A comparative study between freshly processed AR (AR-F) and traditionally processed AR (AR-T) assessed active metabolites, pharmacological effects and mechanisms.ResultsThe appearance and active metabolites content of AR samples were affected by moisture content, kneading, and sweating treatments. After these treatments, the content of polysaccharides and calycosin-7-O-glucoside increased compared to unpretreated samples at the same moisture level. Entropy weight-TOPSIS analysis showed that the sample with 35% moisture, 100 kneading cycles, and 12 h of sweating had the highest score. Comparative studies revealed that AR-F had significantly higher content of total polysaccharides, total flavonoids, and calycosin-7-O-glucoside compared to AR-T, along with an increased flavonoid glycoside/aglycone ratio. However, no significant differences were observed in the total saponins and their metabolites. Pharmacological studies demonstrated that total flavonoids in AR-F exhibited superior macrophage RAW264.7 activation, compared to AR-T. Furthermore, we confirmed that the enhanced immunomodulatory capacity of AR-F was linked to its ability to stimulate the release of TNF, SRC, ER-α, AKT, HSP90, and Caspase-3 in RAW264.7 cells.ConclusionOur study optimized the fresh processing method of AR, and conducted a systematic comparative analysis between fresh and traditional processing samples, providing a basis for post-harvest processing in the AR production areas.

Evaluation of Tourism Ecological Security and Measurement of Regional Differences in China

ABSTRACT Expanding upon the Driver-Pressure-State-Impact-Response (DPSIR) model system, this work employs the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach, Kernel density estimation, Spatial autocorrelation, Dagum Gini coefficient, and geographic detectors to evaluate the ecological security level of China’s tourism industry from 2010 to 2020. Examining the tourism ecological security’s spatio-temporal evolution, geographical disparities, and driving variables is the goal of this study. One thing that stands out from the data is that (1) there is a clear increase trend in the time series of tourism ecological security (TES). (2) There is spatial variability and lack of landscape equilibrium when the general geographical distribution pattern in space gradually decreases from east to west. (3) According to the Dagum Gini coefficient, there is a decreasing geographical non-equilibrium of tourism ecological security, with the main cause of this non-equilibrium being the inter-regional disparity between the east and west. (4) The factors affecting the tourism ecological security are complex and diverse. These factors are closely related to the economy, environment and population of the city. The findings provide a reference for the sustainable development of regional tourism ecology.