Grey Relational Analysis Method Research Articles

Correlation between high temperature performance of gussasphalt mixture for steel bridge decks and rheology of asphalt mastic

Rutting is a prevalent issue in the Gussasphalt (GA) mixture layer of steel bridge deck pavements. This study aims to investigate the correlation between the rheology of GA mastic and the high-temperature performance of the GA mixture. To achieve this objective, GA mastic underwent temperature and frequency sweep tests, along with multiple stress creep and recovery tests employing a dynamic shear rheometer. Additionally, indentation tests, uniaxial penetration tests, and Lueer fluidity tests were conducted on the GA mixture. The Grey Relational Analysis method was employed to evaluate the relationship between asphalt mastic rheology and the high-temperature deformation resistance of the GA mixture. The findings reveal that the addition of mineral filler enhances the deformation resistance of both GA mastic and the mixture. The percent difference in non-recoverable creep compliance (Jnr-diff) was minimized when the weight ratio of filler to asphalt binder (F/B) was 2.5. Furthermore, increasing the F/B ratio from 2.0 to 3.5 resulted in a more than twofold increase in the GA mixture’s shear strength. Asphalt type emerged as a significant factor affecting the indentation parameter of the GA mixture, with an F-value reaching 8.82 based on ANOVA analysis. The rheological properties of GA mastic exhibited a significant correlation with the high-temperature performance of the mixture. The highest Grey Relational Grade of 0.87 was observed between the non-recoverable creep compliance at a 3.2 kPa stress level (Jnr3.2) and indentation. In future research, greater emphasis can be placed on exploring the micromechanical properties of the GA mastic-aggregate interface.

Machining Performance Analysis of Ti6-Al-4V in Powder-Mixed EDM Using Green Dielectric Oil

In this investigation, the factors were oil type ([Formula: see text]), powder type ([Formula: see text]), and powder concentration ([Formula: see text]) level, and the machining parameters were the voltage (V), capacitance (pf), pulse-on time ([Formula: see text]), and pulse-off time ([Formula: see text]). The output parameters were the material removal rate (MRR), and tool wear rate (TWR). Three types of oils and powders, along with their respective particle concentrations, were selected for this experiment. Neem, Karanja, and sesame oils were mixed with aluminum oxide (Al2O3), chromium (Cr), and nickel (Ni) powder at 4, 6, and 8[Formula: see text]gm/l, respectively, using an overhead stirrer to investigate the conditions under which the green oil powder mixed mixture yielded a higher MRR and lower TWR during the micro-EDM process of titanium alloy (Ti-6Al-4V) using a 0.8[Formula: see text]mm tungsten carbide (WC) tool with fixed machining parameters (240[Formula: see text]V, 100[Formula: see text]pf, 20[Formula: see text][Formula: see text]s [Formula: see text], and 5[Formula: see text][Formula: see text]s [Formula: see text]). All the powders used in this experiment had a 32-micron (450 mesh) size. The Taguchi L[Formula: see text] orthogonal array and grey relational analysis methods were used to design and optimize the input process parameters for both responses. SEM, optical microscopic analysis, and EDS analysis were performed to examine the surface morphology, characteristics of the WC tool, and elemental composition of both the tool and the workpiece material. The optimum conditions for Karanja oil with a Cr powder mixture concentration of 4[Formula: see text]g/l resulted in higher MRR and lower TWR. However, machining with neem and sesame oils resulted in higher TWR and lower MRR.

Evaluation of Goalkeepers’ Performance via Grey Relational Analysis Method: Example of Africa Cup of Nations

Aim: The aim of the study is to evaluate the technical performance of goalkeepers via the gray relational analysis method. Materials and Method: The sample of the study consisted of 21 goalkeepers competing in the African Cup of Nations 2023. Data was obtained from the official website of the Confederation of African Football and Transfermarkt. Technical performance indicators were determined as “save percentage”, “claimed crosses” and “passing accuracy”. The data were evaluated by the grey relational analysis method among the quantitative research methods. In this context, six steps of the grey relational analysis method were applied respectively. These are "preparation of the data set and generation of the decision matrix", "normalization of the data", "generation of the reference series", "generation of the absolute value table", "generation of the grey relational coefficient matrix", "calculation and ranking of the grey relational degrees". Results: According to the analysis results, the highest technical performance value was calculated as 0.7654, while the lowest technical performance value was calculated as 0.3949. Thus, the top 3 goalkeepers were determined as Ronwen Williams (South Africa), Edouard Mendy (Senegal), and Babacar Niass (Mauritania). The last 3 goalkeepers were determined as Richard Ofori (Ghana), Ibrahim Koné (Guinea), and Baboucarr Gaye (Gambia). Conclusion: According to the results of the research, Ronwen Williams (South Africa), who won the Golden Glove Award and was selected for the team of the tournament at the Africa Cup of Nations, was determined as the goalkeeper with the highest performance. His national team South Africa completed the tournament in 3rd place, despite being ranked 19th among 24 teams in terms of market value. Similar conclusions were obtained for the remained goalkeepers. It is expected that this conclusion will provide significant contributions for stakeholders such as researchers, goalkeepers, coaches, scouts, and trainers.

Evaluation of the financial performance of electricity generation companies using the Grey relational analysis method

In the study, 10 electricity generation companies on the Fortune 500 list were examined using nine criteria. These companies are listed in the top 40 in terms of installed power. The aim of the study was to determine the financial performances of the companies with the entropy-weighted Grey Relationship Analysis (GRA) method by obtaining the 2021 data of the criteria from the websites of the relevant companies. As a result of the weighting made by the entropy method, it was determined that the most important criterion was the export income. These criteria were determined as equity, total assets, net sales, asset turnover, equity turnover, profit before interest/tax , change in profit before interest/tax, and as change in net sales respectively. After the weights of the criteria were calculated with the entropy method, the GRA method was used to rank the alternatives. Çelikler company was determined to be the best electricity generation company with the highest degree of relationship. This company was followed by Odaş, Gama, İçdaş, Limak, Akenerji, Zorlu, Enerjisa, Aksa and Enka respectively.

Optimization of Machining Parameters on The Geometry Precision of Cortical Screw of Ti-6Al-4V ELI Using Gray Relational Analysis Method

Titanium and its alloys have been widely developed in aerospace, biomedical, electronics, sports, and offshore. In the biomedical field, titanium alloy that has been widely used, based on its properties, is biocompatible for bone implants and not becoming a foreign thing in the body. This study aims to optimize to determine the optimal conditions for cutting parameters for the precision of the cortical thread screw geometry referring to the ISO 5835 standard. In this study, the cutting was carried out using a CNC lathe, where the machining parameters used were spindle rotation of 100, 200, and 300 rpm, depth of cut of 0.01, 0.02, and 0.03 mm, and types of lubricants in the form of synthetic oil, virgin palm oil, and virgin coconut oil. Data processing in this study is analyzed using the Gray Relational Analysis Method to get an optimal combination of cutting conditions. The significant factor that influences the precision of cortical thread is the depth of cut, which is a contribution of 98%. It can illustrate that the smaller depth of cut will produce better screw geometry. The optimum conditions for cutting parameters that deliver the best level of precision were obtained at a spindle speed of 100 rpm, depth of cut of 0.01 mm, and type of lubricant of coconut oil. Meanwhile, the screw surface damage is stacked chips as a result of the high temperature generated during the cutting process.

Short-term load forecasting of regional integrated energy system based on spatio-temporal convolutional graph neural network

Accurate short-term load forecasting has significant reference value for the preliminary planning and subsequent coordinated control of regional integrated energy systems. However, main limitations are: (1) Insufficient exploration of the complex correlations among diverse loads and between loads and meteorological factors; and (2) Feature information extraction only from the time dimension, neglecting the spatial dimension, resulting in incomplete feature information extraction. This paper proposes a new method for short-term load forecasting, namely the Attention Spatial–temporal Graph Convolutional Network (ATSTGCN) method. Firstly, the Grey Relational Analysis (GRA) method is used to analyze the correlation between diverse loads, meteorological features, and load features, constructing the input data for the graph neural network. Then, synchronous graph convolution is introduced to extract information between features. Next, the spatio-temporal attention mechanism is incorporated into the synchronous graph convolution to capture spatio-temporal features and reduce information loss using a spatio-temporal window. Finally, a short-term load forecasting method based on ATSTGCN for regional integrated energy systems is constructed. Extensive simulation experiments are conducted on the publicly available dataset from Arizona State University’s online system. The experimental results show that compared to other methods, the proposed method improves MAPE, MAE, and RMSE by 5.6%, 11.7%, and 11.6%.

Selection and application of building material suppliers based on the 2-tuple linguistic neutrosophic number combined grey relational analysis method

The quality of building materials will affect the implementation effect of construction projects. To ensure the service capacity of building materials, it is necessary to do a good job in selecting suppliers. In the specific evaluation of building material suppliers, after evaluation, suppliers with poor quality are excluded to ensure the quality of material supply, reasonably improve the construction effect of the building project, meet the construction needs of the building project, and improve the quality of the building project. The selection and application of building material suppliers (BMSs) is a multiple-attribute group decision-making (MAGDM) technique. In this study, the 2-tuple linguistic neutrosophic number combined grey relational analysis (2TLNN-CGRA) technique is constructed based on the classical grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). Finally, a numerical example for building material supplier selection was constructed and some comparisons is constructed to illustrate the 2TLNN-CGRA technique. The main contribution of this study is constructed: (1) the 2TLNN-CGRA technique is implemented to cope with the MAGDM under 2TLNSs; (2) the 2TLNN-CGRA technique is implemented in line with the 2TLNN Hamming distance (2TLNNHD) and 2TLNN Euclidean distance (2TLNNED) simultaneously under 2TLNSs; (3) the numerical example for building material supplier selection is implemented to show the 2TLNN-CGRA technique; and (4) some efficient comparative studies are constructed with several existing decision techniques.

Research on the Level of Synergistic Development of Supply and Demand in China’s Health Industry

This study combines the latest vague set similarity measurement method with the entropy weight TOPSIS method; furthermore, it applies the grey relational analysis method to establish a model for the synergistic development of supply and demand in the health industry, and it empirically analyzes the development status, synergy level, and synergistic development level of China’s health industry supply and demand from 2013 to 2021. The findings reveal that: (1) China’s health industry supply maintains steady growth, while demand grows rapidly. However, the overall level of supply and demand development is generally low, indicating significant potential for future development. Moreover, the development of the supply lags behind that of the demand, leading to an increasing gap year by year. (2) The level of synergy between supply and demand in China’s health industry is relatively high but is decreasing year by year. Although the degree of synergistic development between supply and demand continues to increase, it remains at a low level, indicating that high coordination between supply and demand comes at the cost of a low development level. (3) There exists a significant regional imbalance in the development of China’s health industry, with the overall difference showing a widening trend year by year. Regional disparities are the main source of the overall difference.

Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model

INTRODUCTION: It is of great research significance to explore whether China can achieve the "two-carbon target" on time. The MLP model combines nonlinear modeling principles with other techniques, possessing powerful adaptive learning capabilities, and providing a viable solution for carbon emission prediction.
 OBJECTIVES: This study models and forecasts carbon emissions in Jiangsu Province, one of China's largest industrial provinces, aiming to forecast whether Jiangsu province will achieve the two-carbon target on time plan and provide feasible pathways and theoretical foundations for achieving dual carbon goals.
 METHODS: Based on the analysis of the contributions of relevant indicators using the Grey Relational Analysis method, a comprehensive approach integrating the STIRPAT model, Logistic model, and ARIMA model is adopted. Ultimately, an MLP prediction model for carbon emission variations is established. Using this model, simulations are conducted to analyze the carbon emission levels in Jiangsu Province under different scenarios from 2021 to 2060.
 RESULTS: The time to reach carbon peak and the likelihood of achieving carbon neutrality vary under three scenarios. Under the natural scenario of no human intervention, achieving carbon neutrality is not feasible. While under human-made intervention scenarios including baseline and intervention scenarios, Jiangsu Province is projected to achieve the carbon neutrality target as scheduled, attaining the peak carbon goal, however, proves challenging to realize by the year 2030.
 CONCLUSION: The MLP model exhibits high accuracy in predicting carbon emissions. To expedite the realization of dual carbon goals, proactive government intervention is necessary.

Comprehensive Assessment of NDVI Products Derived from Fengyun Satellites across China

NDVI data are crucial for agricultural and environmental research. The Fengyun-3 (FY-3) series satellites are recognized as primary sources for retrieving NDVI products on a global scale. To apply FY-3 NDVI data for long-term studies, such as climate change, this study conducted a thorough evaluation to detect the potentials of the FY-3B and FY-3D satellites for generating a long time series NDVI dataset. For this purpose, the spatiotemporal consistency between the FY-3B and FY-3D satellites was evaluated, and their performances were compared. Then, a grey relational analysis (GRA) method was applied to detect the factors influencing the consistency among the different satellites, and a gradient boosting regression (GBR) model was constructed to create a long-term FY-3 NDVI product. The results indicate an overall high consistency between the FY-3B and FY-3D NDVIs, suggesting that they could be used as complementary datasets for generating a long-term NDVI dataset. The correlations between the FY-3D NDVI and the MODIS NDVI, as well as the leaf area index (LAI) measurements, were both higher than those of FY-3B, which indicates a better performance of FY-3D in retrieving NDVI data. The grey correlation degrees between the NDVI differences and four parameters, which were land cover (LC), DEM, latitude (LAT) and longitude (LON), were calculated, revealing that the LC was the most related to the NDVI differences. Finally, a GBR model with FY-3B NDVI, LC, DEM, LAT and LON as the input variables and FY-3D NDVI as the target variable was established and achieved a robust performance. The R values between the GBR-estimated NDVI and FY-3D NDVI reached 0.947, 0.867 and 0.829 in the training, testing and validation datasets, respectively, indicating the feasibility of the established model for generating long time series NDVI data by combining data from the FY-3B and FY-3D satellites.

Personnel Selection Based on the LBWA, TOPSIS and GRA Methods: A Case Study on Foreign Trade Company

Recruitment and personnel selection are affected by significant factors. Thus, personnel selection is one of the main decision-making problems for a company’s long-term survival. The objective of this study is to identify the most suitable candidate for the export department of a company operating in Mersin, using the Level Based Weight Assessment (LBWA)-based Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA) methods. The criteria were determined based on the literature review and experts’ opinions. The weight of criteria was calculated by the LBWA method, and the alternatives (candidates) were ranked using the TOPSIS and GRA methods. The LBWA results showed that fluency in a foreign language and team player were the most and least important criteria, respectively. The results from both methods (TOPSIS and GRA) suggested different candidates for the relevant positions. Furthermore, sensitivity analyses were conducted to assess the validity and robustness of the results. In conclusion, the findings of this study provide valuable insights to decision-makers involved in the personnel selection process.

Optimizing biochar application for enhanced cotton and sugar beet production in Xinjiang: a comprehensive study.

Optimizing biochar application is vital for enhancing crop production and ensuring sustainable agricultural production. A 3-year field experiment was established to explore the effects of varying the biochar application rate (BAR) on crop growth, quality, productivity and yields. BAR was set at 0, 10, 50 and 100 t ha-1 in 2018; 0, 10, 25, 50 and 100 t ha-1 in 2019; and 0, 10, 25 and 30 t ha-1 in 2020. Crop quality and growth status and production were evaluated using the dynamic technique for order preference by similarity to ideal solution with the entropy weighted method (DTOPSIS-EW), principal component analysis (PCA), membership function analysis (MFA), gray relation analysis (GRA) and the fuzzy Borda combination evaluation method. Low-dose BAR (≤ 25 t ha-1 for cotton; ≤ 50 t ha-1 for sugar beet) effectively increased biomass, plant height, leaf area index (LAI), water and fertility (N, P and K) productivities, and yield. Biochar application increased the salt absorption and sugar content in sugar beet, with the most notable increases being 116.45% and 20.35%, respectively. Conversely, BAR had no significant effect on cotton fiber quality. The GRA method was the most appropriate for assessing crop growth and quality. The most indicative parameters for reflecting cotton and sugarbeet growth and quality status were biomass and LAI. The 10 t ha-1 BAR consistently produced the highest scores and was the most economically viable option, as evaluated by DTOPSIS-EW. The optimal biochar application strategy for improving cotton and sugar beet cultivation in Xinjiang, China, is 10 t ha-1 biochar applied continuously. © 2024 Society of Chemical Industry.

Taguchi–gray relational analysis of eccentricity and tilt of multilens module mount fabricated by injection compression molding

AbstractThis study utilizes the mold flow analysis technique of the injection compression molding (ICM) process, combined with the Taguchi–gray relational analysis (GRA), for process optimization analysis on the roundness of the lens holes and the flatness of the lens mount in a 4 × 4 planar multilens array mount. After manufacturing simulation analysis, the eccentricity and tilt information of the lens mount was further evaluated optically through spot diagram analysis upon inserting the same glass lenses. The results showed a positive correlation trend between roundness and flatness in structural deformation analysis, indicating that improving the overall flatness of the lens mount can enhance the roundness of the lens holes. In optical analysis, better improvements in the lens tilt angle were achieved through GRA. In conclusion, aiming to simultaneously improve the roundness of the lens holes and the overall flatness of the lens mount, the Taguchi–GRA method can achieve the optimization objectives. In terms of optical performance, by optimizing for roundness, it is possible not only to reduce the diameter of the light spot but also to simultaneously reduce the offset displacement of the light spot center on the screen. The method proposed in this paper can serve as an analytical model for the design and fabrication of plastic multilens mount.

Investigation and Optimization on Parameters of Gas Additive Powder Mixed Near Dry –EDM (GAPMNDEDM) by using Taguchi based- Grey Relational Optimization

The Gas Additive Powder Mix near Dry Electric Discharge Machining (GAPMND-EDM) serves a manufacturing purpose, specifically for cutting hard materials. This involves the removal of material from a workpiece via fusion, ablation, and evaporation caused by the heat energy generated through electric sparks during energy supply. The process offers several advantages in terms of performance and characteristics. This research project aims to optimize key process parameters, including material removal rate, surface roughness, residual stresses, and microhardness. The study employs a methodology that combines the standard deviation-based objective weighting method with GRA (Gray Relational Analysis) optimization to enhance the hybrid GAPMND-EDM process when applied to EN-31 material. Experimental runs were conducted to evaluate the impact of various input factors, such as pulse-on time, discharge current, dielectric fluid pressure, and metallic powder concentration. The taguchi-based GRA method was utilized for this purpose, and the experimental design followed an L-27 orthogonal array with the assistance of Minitab-19 software. A total of 27 experiments were performed, encompassing diverse combinations of process parameters. Subsequently, an ANOVA (Analysis of Variance) was executed to analyze the influence of pulse-on time, discharge current, dielectric fluid pressure, and metallic powder concentration on Material Removal Rate (MRR), Surface Roughness (SR), Residual Stress (RS), and Microhardness (MH). The result shows the optimal combination of parameters, denoted as A2B2C2D1, was identified as the preferred configuration.

Comparative Analysis of the Combination of MOORA and GRA with PIPRECIA Weighting in the Selection of Warehouse Heads

The Head of Warehouse has the main responsibility to manage and supervise the daily operations of the warehouse, including receiving, storing, and dispensing goods. In addition, the warehouse head is responsible for leading the warehouse staff team, providing the necessary direction, training, and supervision to ensure efficiency and safety in warehouse operations. This study aims to select warehouse heads by applying a combination of MOORA and PIPRECIA methods, as well as GRA and PIPRECIA. The combination of MOORA and PIPRECIA as well as GRA and PIPRECIA will be compared based on the final ranking of the two methods used. Based on the ranking results on the MOORA Method, the highest value with a value of 0.40017 was obtained by FHY with rank 1, a value of 0.36637 was obtained by AGL with rank 2, and a value of 0.35721 was obtained by YLS with rank 3 Based on the rating results on the GRA Method, the highest value with a value of 0.12164 was obtained by AGL with rank 1, a value of 0.12017 was obtained by FHY with rank 2, and a value of 0.1054 obtained by TAN with rank 3. Ranking differences between Multi-Method Objective Optimization by Ratio Analysis (MOORA) and Gray Relational Analysis (GRA) can arise due to differences in approaches in evaluating and comparing alternatives. The results of the conformity test can be concluded in the selection of warehouse heads who recommend the GRA method compared to the MOORA method, because the results of the conformity level of the GRA method get a value of 99.99919% higher than the MOORA method.

Multi-objective optimization of temperature uniformity in the immersion liquid cooling cabinet with Taguchi-based grey relational analysis

Single-phase immersion liquid cooling technology can meet the cooling needs of high-density Data Centers. To improve the temperature uniformity in an immersion liquid cooling cabinet and prevent local hot spots that reduce the efficiency of servers, the structural parameters of the cabinet were optimized in this study. The fluid path is mainly affected by the position of inlet and outlet, length, Angle and number of baffles. To evaluate the effects of these factors on the temperature uniformity in the cabinet, Taguchi-based grey relational analysis method and computational fluid dynamics were used to optimize the system, the standard deviation and maximum temperature difference are determined as evaluation indices, and L16 (44) orthogonal array was used for experimental design. The baffle Angle is judged to be the most important structural parameter by the Main Effect Analysis, whose contribution rate is 49.23%. The multi-objective optimization is transformed into a single objective by the grey relational analysis, and the influence rate of each structural parameter is shown. The optimal combination of structural parameters is determined. The results show that the temperature uniformity of the optimized cabinet is improved by 46%, which provides a reference for the design of immersion liquid cooling Data Centers.

Formulation and Analysis of Handwritten Batik Wax Quality from Recycled Materials

<p><em>U</em><em>sed cooking oil can be utilized as an alternative raw material for making batik wax. Recycling batik wax involves using recycled materials such as used wax, used cooking oil, and other substances like paraffin, microwax, colophonium resin, and damar resin. The recycling of used wax and the utilization of used cooking oil are crucial for the batik industry as they reduce production costs and environmental pollution.</em><em> A </em><em>combination of Taguchi and Grey Relational Analysis (GRA) method is employed to obtain optimal values for the response characteristics of line width continuity and permeability of</em><em> </em><em>wax. Recycled and new wax is applied to mori fabric and compared based on batik quality criteria.</em><em> </em><em>The research results indicate that the optimal composition for recycled wax consists of 510 grams of used wax (41.5%), 36 grams of used cooking oil (2.9%), 102 grams of paraffin (9.8%), 84 grams of microwax (6.8%), 300 grams of colophonium resin (24.4%), and 180 grams of damar resin (14.6%). According to batik experts' assessments, the quality of recycled batik wax is equivalent to that of new batik wax. Recycled batik wax has the melting point of 67.9 °C, the viscosity value of 90.15 cP, and the production cost of Rp 30.909,00.</em></p>

Optimizing the five magnetic abrasive finishing factors on surface quality using Taguchi-based grey relational analysis

The effectiveness of the magnetic abrasive finishing (MAF) process relies on several factors, including the brush’s flexibility that varies across tools. This study aimed to optimize the results of five key parameters (voltage, finishing time, gap distance, rotating speed, and particle size) on surface roughness (SR) and microhardness (HV) using the grey relational analysis (GRA) method. Experimental work employed the Taguchi design with L27 trials in Minitab 17, involving five variables with three levels for each. The impact of these parameters on microhardness and surface roughness for stainless steel SUS420 bubble cups was assessed using Taguchi and regression analyses. The best roughness improvement and the most substantial enhancement in microhardness were individually obtained with the GRA method. This method assigned the best results for both surface roughness and microhardness. According to Taguchi analysis, the voltage parameter has the main or maximum parameter effect on grade, followed by gap distance, time, spindle speed, and particle size. It was found that the optimal parameters were the same as the input parameters.

Combination of Grey Relational Analysis (GRA) and Simplified Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA-S) in Determining the Best Staff

Problems in selecting the best staff often involve complex challenges such as difficulty finding candidates with good performance. The problems faced in the selection of the best are only based on the assessment of discipline and productivity of performance carried out by the staff, so the assessment process does not use aspects of criteria that are considered important in selecting the best staff. This study aims to determine the best staff based on predetermined criteria and in determining the selection of the best staff using the Gray Relational Analysis (GRA) decision model while in determining the weight of criteria using the Simplified Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA-S) model so that the weight of the resulting criteria is not based on assumptions from decision makers. The results of the best staff assessment ranking using the Gray Relational Analysis method and the Simplified Pivot Pairwise Relative Criteria Importance Assessment weighting method obtained the results, namely for Rank 1 obtained by Denis Irawan with a final Gray Relational Analysis value of 0.243014. The results of data processing in the TRITAM Model test for the best staff selection application were adjusted to the conclusion of the overall results of the TRITAM Model criteria for technology acceptance, the results were good at 82.56%.

Optimizing irrigation schedules of greenhouse tomato based on a comprehensive evaluation model

In the context of climate change, water availability has emerged as a pressing concern in agriculture, indicating the urgency to enhance irrigation management. Nevertheless, accurately quantifying irrigation schedules in diverse climates and soil textures remains a complex challenge. Here, a three-year irrigation experiment (2020–2022) of greenhouse tomato was conducted at three typical locations (Yulin, Yangling and Hanzhong) in northwest China. The irrigation schedules were determined by the cumulative evaporation (CE) measured from a 20 cm pan. A total nine treatments were set by combining three irrigation frequencies (IF) with three single doses (ID). Twenty indices related to tomato growth, photosynthesis, yield and water use, fruit quality, and environmental benefit were evaluated comprehensively by using TOPSIS (technique for order preference by similarity to ideal solution) and GRA (grey relational analysis) methods of combining weights based on game theory. The results showed that the indicator with highest weight was yield, followed by crop water productivity and vitamin C. The optimal ranges of irrigation frequency (irrigation dose) were determined as follows: 10.00–11.08 mm (0.81–0.90 CE) in Yulin, 13.12–15.68 mm (0.77–0.89 CE) in Yangling, and 11.44–16.40 mm (0.67–0.77 CE) in Hanzhong, respectively. This study introduces a method applied to prioritize the key indicators for irrigation scheduling, providing a reference for optimizing irrigation practices in greenhouse tomato production and improving water management in agriculture.