Weight Calculation Research Articles

Application of the PSI Method in Selecting Sustainable Energy Development Technologies

The development of renewable energy is not only an urgent solution for addressing climate change but also a driving force for sustainable economic growth. The transition to clean, inexhaustible energy sources not only helps to reduce greenhouse gas emissions and protect the environment but also ensures national energy security, creates employment opportunities, and enhances the quality of life for individuals. Presently, various technologies exist for sustainable energy development, each characterized by multiple criteria, complicating the evaluation of their performance. This study presents a straightforward method for identifying the best option among eight sustainable energy development alternatives: hydropower, geothermal, biomass, wind, solar, concentrated solar power, coal technology, and oil-fired power plants, each of which is characterized by 17 distinct criteria. The simple method utilized is the Preference Selection Index (PSI) method, which eliminates the need for criteria weighting. This absence of criteria weight calculation in the PSI method distinguishes it from other ranking techniques that typically require such calculations. Therefore, the PSI method significantly simplifies the comparison of the available options compared to other ranking methods, as it bypasses the need for criteria weight calculations. The optimal option identified through the PSI method was also compared with the optimal option identified using 6 other methods: Multi Atributive Ideal Real Com parative Analysis (MAIRCA), Evaluation Based on Distance from Average Solution (EDAS), COmplex PRroportional ASsessment (COPRAPS), Multiobjective Optimization On the basis of Ratio Analysis (MOORA), Proximity Indexed Value (PIV), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Notably, all employed methods consistently identified geothermal energy as the optimal choice.

Evaluation of tourism ecological security and its obstacles in semi-arid river valley urban: a case study of Lanzhou, China

Assessing tourism ecological security is essential for balancing development with environmental sustainability, thereby guiding effective resource management and policy formulation within the tourism industry. This study evaluates the ecological security of Lanzhou’s tourism sector, a city rich in historical and natural resources yet facing unique ecological challenges. Our objectives were to assess the current status of tourism ecological security in Lanzhou and provide actionable policy recommendations. We employed a comprehensive evaluation framework that integrates both qualitative and quantitative methods, including the DPSIR model, mean square deviation, and entropy methods for weight calculation, as well as TOPSIS for overall assessment and coupled coordination degree and obstacle degree models for subsystem analysis. The results indicate an upward trend in Lanzhou’s tourism ecological security from 2009 to 2021, peaking in 2019 before experiencing a pandemic-induced decline in 2020–2021. While subsystem coupling coordination generally improved, the pressure and state subsystems exhibited instability. These findings offer valuable insights for similar regions and underscore the necessity for holistic management strategies. We recommend enhancing ecological monitoring, promoting sustainable tourism practices, and enhancing local community involvement in tourism management. These measures are crucial to improving Lanzhou’s tourism ecological security and could serve as a model for other cities with similar development trajectories. Future research should focus on refining ecological security evaluation models and exploring the broader socio-economic impacts of ecological sustainability in tourism.

Encrypted Search Method for Cloud Computing Data Under Attack Based on TF-IDF and Apriori Algorithm

ABSTRACT This paper designs the MKSE and SEMSS methods. Among them, MKSE uses an improved TF-IDF weight calculation method to extract keywords and applies virtual keywords to construct inverted indexes, making it difficult for malicious attackers to infer the index content easily. SEMSS uses the Apriori algorithm to mine the co-occurrence relationship between words and find the keyword set that meets the minimum support threshold to improve the recall rate of search results. Finally, the security of the scheme is verified from the aspects of semantic security, effici this paper designsency, data integrity, etc. The results showed that the data encryption time of MKSE and TRSE methods increased gradually with the increase in document collection storage. The index build time was increased as the document set grew. The accuracy of the improved TF-IDF method was 63.8%. The running time of Apriori decreased with the increase of minimum support. When the minimum support was 12.0%, the Apriori algorithm ran for 211 seconds. The MKSE method was more efficient than the TRSE method in searching documents by query keywords. When the document set size was 3000, the SEMSS method had a full search rate of 81.09%. This research realizes the semantic security of outsourced data, which can efficiently and comprehensively carry out cryptographic retrieval based on keyword sorting.

Prioritization Suitable Hydropower Potential Sites Along the Furfuro River, Ethiopia

Energy access is a critical factor for economic development and social progress, particularly in rural areas of developing countries like Ethiopia. This study aims to identify and prioritize suitable hydropower sites along the Furfuro River using a multi-criteria decision analysis (MCDA) approach, specifically the analytic hierarchy process (AHP). Five key criteria were considered: power, discharge, head, accessibility, and distance. A pairwise comparison matrix was constructed to assess the relative importance of these criteria. Normalization and critical weight calculation were performed to determine the weight of each criterion. The consistency of the pairwise comparison matrix was evaluated using the consistency ratio (CR), which was found to be acceptable. The site with the highest suitability index was ranked the most suitable for hydropower development. The findings of this study provide valuable insights for decision-makers in prioritizing hydropower development in the region, reducing reliance on traditional, harmful energy sources, and improving the quality of life for local communities along the Furfuro River. By considering multiple factors and employing the Analytic Hierarchy Process, this research contributes to sustainable energy planning and resource management.

Comprehensive benefits evaluation of low impact development using scenario analysis and fuzzy decision approach

The comprehensive benefit evaluation of LID based on multi-criteria decision-making methods faces technical issues such as the uncertainties and vagueness in hybrid information sources, which can affect the overall evaluation results and ranking of alternatives. This study introduces a multi-indicator fuzzy comprehensive benefit evaluation approach for the selection of LID measures, aiming to provide a robust and holistic framework for evaluating their benefits at the community level. The proposed methodology integrates quantitative environmental and economic indicators with qualitative social benefit indicators, combining the use of the Storm Water Management Model (SWMM) and ArcGIS for scenario-based analysis, and the use of hesitant fuzzy language sets and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for decision-making. The framework’s novelty lies in the integration of the hesitant fuzzy weighted average algorithm to handle subjective uncertainties in expert judgment and the incorporation of multi-return period scenarios to enhance the robustness of the evaluation. The comprehensive benefits of 26 LID configurations were conducted in Chenglong Road Subdistrict under five rainfall return period scenarios of 5, 10, 20, 50, and 100 years. The results show that LID measures, particularly combinations of sunken green spaces and permeable paving, offer significant reductions in runoff and peak flow, along with improved flood mitigation across multiple return periods. Additionally, this study identifies practical LID implementation priorities for local decision-makers. The relative closeness is influenced by the indicators and non-calibrated parameters. However, it overall does not affect the main trends and key insights derived. The robustness of the proposed approach is reinforced by four key aspects: the impact of the Thiessen polygon method in ArcGIS, the influence of composite runoff coefficient and iterative optimization in SWMM, the effect of hesitant fuzzy linguistic sets and TOPSIS on weight calculation, and the contribution of simulations under different return periods to stability analysis.

Research on the tourism service quality evaluation of Gongbei Lingnan community under the perspective of SERVQUAL theory

The Lingnan community, located between Macau and Zhuhai City in China, serves as a connecting port community that attracts a growing number of tourists for leisure and business. The Lingnan community faces the challenge of improving tourism services to meet the ever-increasing needs of tourists with the continuous growth of tourists. Currently, the community’s tourism service facilities are relatively scarce, and the service quality needs to be improved. Hence, improving tourists’ satisfaction with Lingnan tourism services is essential. The SERVQUAL Model is utilized in this research to assess the satisfaction of tourism services in the Lingnan community. The data were collected from 254 tourists who visited the Lingnan community. After passing reliability and validity tests, factor analysis was used to determine the weights of the primary indicators, followed by entropy weight calculations to measure the weights of secondary indicators further, and finally presented as an IPA matrix. The survey results show that the second-level dimension factors under the tangible, reliability, assurance, and security dimensions significantly impact tourist satisfaction. The study shows that tourist satisfaction predominantly affects the sustainable development of community tourism, so the community must focus on ensuring better service quality. Enhancing the attractiveness of Lingnan community tourism is conducive to the joint development of the tourism industry in Zhuhai and Macau but also has essential significance in strengthening the friendship between Zhuhai and Macau.

Weight Optimization of the Electric Bus Carbody Structure

Car body is a lightweight construction unit in the form of welded steel construction, has high strength and rigidity against dynamic loading without a fixed change in shape. In this study, the design of the car body bus frame consisted of roof, mascara, side wall, end wall, front part, and underframe. All of these components are designed using Autodesk Inventor Professional 2020 software. The frame material used is SS201. For data retrieval purposes, weight calculations are used in Autodesk Inventor. In the study, we succeeded in making the car body design according to regulations, measuring the weight of the car body, and optimizing the car body. The results showed that with a little optimization on the side wall frame, the weight of the car body bus can be reduced by 11.7% from the initial weight of 461.5 kg to 407.4 kg without changing its characteristics.

User preference interaction fusion and swap attention graph neural network for recommender system.

Recommender systems are widely used in various applications. Knowledge graphs are increasingly used to improve recommendation performance by extracting valuable information from user-item interactions. However, current methods do not effectively use fine-grained information within the knowledge graph. Additionally, some recommendation methods based on graph neural networks tend to overlook the importance of entities to users when performing aggregation operations. To alleviate these issues, we introduce a knowledge-graph-based graph neural network (PIFSA-GNN) for recommendation with two key components. The first component, user preference interaction fusion, incorporates user auxiliary information in the recommendation process. This enhances the influence of users on the recommendation model. The second component is an attention mechanism called user preference swap attention, which improves entity weight calculation for effectively aggregating neighboring entities. Our method was extensively tested on three real-world datasets. On the movie dataset, our method outperforms the best baseline by 1.3% in AUC and 2.8% in F1; Hit@1 increases by 0.7%, Hit@5 by 0.6%, and Hit@10 by 1.0%. On the restaurant dataset, AUC improves by 2.6% and F1 by 7.2%; Hit@1 increases by 1.3%, Hit@5 by 3.7%, and Hit@10 by 2.9%. On the music dataset, AUC improves by 0.9% and F1 by 0.4%; Hit@1 increases by 3.3%, Hit@5 by 1.2%, and Hit@10 by 0.2%. The results show that it outperforms baseline methods.

GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODELING ON LIFE EXPECTANCY RATE IN SOUTH SULAWESI

Geographically Weighted Panel Regression (GWPR) is one of the panel data regression approaches used in spatial data analysis. This study uses the global Fixed Effect Model (FEM) panel regression model and the local GWPR model to examine Life Expectancy Rate (LER) at the district/city level in South Sulawesi Province in 2019-2021. LER is an important indicator that reflects the health and welfare of the community. This research aims to develop a GWPR model that can explain variations in LER and identify factors that affect that variable, so that it can help stakeholders in allocating resources and designing effective intervention programs. Parameter estimation in the GWPR model is carried out in each observation area using the Weighted Least Square (WLS) method. The calculation of spatial weights in the GWPR model used weighting functions such as fixed bi-square, fixed gaussian, fixed exponential, adaptive bi-square, adaptive gaussian, and adaptive exponential. The results showed that the use of a fixed exponential weighting function gave optimal results with the lowest cross-validation (CV) value of 44,614. Parameter analysis of the GWPR model shows that the factors that affect LER are local and not the same in each district/city in South Sulawesi Province. Factors that have a significant influence include the number of health facilities and households that have access to proper sanitation. This GWPR model has a coefficient of determination of 97,7%. The FEM model has a coefficient of determination of 58,4%. Therefore, GWPR performs LER modelling more effectively than FEM.

Application of Simple Additive Weighting (SAW) Method in Selecting the Best Employee Performance (Case Study: CV. Syntax Corporation Indonesia)

This study discusses the application of the Simple Additive Weighting (SAW) method in selecting the best employee performance at CV. Syntax Corporation Indonesia. The background of the problem involves challenges in assessing and selecting the best employees objectively amidst the many assessment criteria. The purpose of the study is to implement the SAW method to improve accuracy and objectivity in performance assessment. The method used is SAW with predetermined criteria, as well as weight calculations and data normalization. The results of the study indicate that the SAW method is effective in facilitating the eval_uation process and producing more accurate employee rankings based on relevant criteria. The results of this study indicate that the SAW method can identify employees with the best performance regularly and transparently. The application of this method produces employee rankings that are consistent with the eval_uation objectives and company needs. By using SAW, CV. Syntax Corporation Indonesia is able to improve the accuracy of the assessment and provide more objective decisions in selecting the best employees. This study suggests that the SAW method be applied more widely for performance-based decision making in other companies.

A CASE STUDY: FUZZY LOGIC BASED DECISION-MAKING SYSTEM FOR ELECTRIC VEHICLE CHARGING

Recently, the use of environmentally friendly electric vehicles instead of traditional internal combustion engine vehicles continues to be widespread due to threats to world life such as global warming and climate change. However, the biggest disadvantages of this technology are the limited range of electric vehicles, long charging times, low number of charging stations, and different charging costs. There is a need for more studies on the problem of finding charging stations, especially for long-distance traveling with electric vehicles. In this paper, a fuzzy logic based decision making system is designed for electric vehicle users to find the most suitable one among the charging stations on a long travelling route. In this study, a traveling route of 1779 km between Izmir and Van provinces in Turkey is selected. The current charging station locations obtained from different charging station companies on this route were processed on Google Earth, and charging stations that were too far from the route were not taken into consideration. A fuzzy logic model was created for 56 charging stations on the route, which performs weight calculation according to the current charging cost and the distance of the station to the normal route. The fuzzy control system is expected to decide on the most appropriate charging station in accordance with the specified rule table, and the results are evaluated.

Health and Well-Being in the Context of Health-Promoting University Initiatives: Protocol for a Mixed Methods Needs Assessment Study at Qatar University.

Health-promoting universities are dedicated to fostering learning environments and organizational cultures that support the physical and mental well-being of students, faculty, and staff. As students constitute the largest group within the university community, any policy intervention targeting them is likely to have a significant impact on the university as a whole. This study aims to assess the health status and needs of Qatar University (QU) students using a comprehensive and holistic definition of health, informed by the perspectives of students, faculty members, and key informants. The ultimate goal is to inform evidence-based policies and services designed to improve students' physical and mental well-being. An explanatory sequential mixed-methods research design will be used to conduct a comprehensive assessment of students' health status and needs. This assessment will consist of a quantitative component (a web-based health survey) administered to a convenience sample of students, and a qualitative component, including focus groups with students and faculty members, as well as interviews with key informants. Priority health issues and their determinants, identified through the quantitative assessment, will inform and guide the qualitative assessment to provide a deeper understanding of the various contexts and factors shaping them. Descriptive analyses (eg, proportions or means with SDs), comparative analyses (eg, t tests or chi-square tests), and association analyses (eg, linear, logistic, or Poisson regression models) will be used to analyze the quantitative data. Thematic analysis will be used in the qualitative assessments. Additionally, an environmental scan will be conducted to assess relevant facilities, services, and programs at the QU campus and the QU Primary Healthcare Corporation Center, as well as to review university policies and regulations that may affect students' health and well-being. Together, the needs assessment and environmental scan will inform the design of multilevel interventions, including health education and promotion programs, health services orientation, and proposed policy changes. Between March and December 2022, 812 students completed the web-based health survey. Data have been extracted, cleaned, and harmonized. Analyses to assess the extent of selection bias and the calculation of weights to account for this in all subsequent analyses have been completed (by December 2023). Following the completion of all quantitative data analyses (expected by the end of 2024), focus groups, interviews, and the environmental scan will begin in January-December 2025. This project will help identify and prioritize the health needs of QU students and their determinants, and inform relevant services and policies targeting these needs. By using comprehensive and context-appropriate methods, this project will contribute to QU's strategic efforts to become a Health-Promoting University. DERR1-10.2196/58860.

Urban Greenway Planning and Designing Based on MGWR and the Entropy Weight Method

Travelers’ attention to high-quality human habitats is increasing, and the role of urban greenways in improving the quality of travelling spaces has also been appreciated. This research aims at making the weight calculation of suitability more scientific and reasonable, clustering the shared bicycle travelling OD points according to suitability, and analyzing the distribution of OD points. Taking Xiamen as an example, multiscale geographically weighted regression and entropy weight methods were used to calculate the weights of variables using multi-source big data. The clustering of origin-destination (OD) points for shared bicycle travel are identified using the DBSCAN clustering algorithm, which can provide accurate support for greenway planning and shared bicycle placement. The results show that the density of tourist attractions, POI entropy index, road density, and intermediate are four important factors affecting the suitability of greenways. The clustering results of the shared bicycle OD points show that the high-aggregation areas of origin and destination points are located in the northeast and southwest directions as well as west and east directions. This study provides a theoretical and modelling analysis reference for greenway planning and design.

PARAMETRIC OPTIMIZATION OF ABRASIVE WATER JET MACHINING PROCESS USING COPRAS AND VIKOR METHODS

Abrasive Waterjet Machining (AWJM) is one of the widely used non-traditional machining process to cut the hard materials like aluminium metal matrix composites (AMMCs) and the materials which are very difficult to machine using conventional machining due to their high hardness and tendency to cause tool wear. Present work two popular multi criteria decision making (MCDM) techniques Complex Proportional Assessment (COPRAS) AND VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) Methods have been used to optimize the AWJM machining process parameters for the machining of AMMCs. Both equal weight and standard deviation methods are used to find the weight of three response parameters like material removal rate, surface roughness and kerf width. From the results it is observed that there is a strong correlation between the weight calculation and the MCDM techniques which shows the robustness of the techniques. It is also noticed that machining with highest traverse speed and with out silicon carbide (SiC) is giving good response paramètres. However machining with low traverse speed is giving worst response parameters.

Inspecting the Antimicrobial, Bio‐Catalytic, DNA Interactions, In Vitro Cytotoxicity, and Cell Cycle Analysis Efficiency of Newly Nano‐Sized Co (II), Ni (II), Cu (II), and Zn (II) Complexes of Schiff's Base Ligand: Spectroscopic Characterization and Quantum Chemical Calculations

ABSTRACTNano‐sized bivalent metal complexes of Cu (II) (C1), Ni (II) (C2), Co (II) (C3), and Zn (II) (C4) were prepared and characterized of the form [M(L)2] where L = 2‐((E)‐((4‐methyl‐2‐(((E)‐thiophen‐2‐ylmethylene)amino)phenyl)imino)methyl)benzenethiol] (C19H16N2S2), M = Cu (II), Ni (II), Co (II), and Zn (II). Elemental analyses, molecular weight calculations, infrared, 1H, 13C, and 15N NMR, electronic, mass, magnetic susceptibility, and conductivity assessments were employed to evaluate our bivalent metal complexes. DFT studies were used to study the tautomeric equilibrium of the ligand (HL), via the DFT\B3LYP technique in connection with a 6–311G* correlation consistent basis set. The metal formed four coordinated with the tridentate N2S donor Schiff base to form octahedral geometry complexes. The XRD analysis of the divalent metal complexes revealed sharp, intense diffraction peaks, indicating their crystalline nature with nanoscale particle sizes. Additional evidence was provided by EDX, SEM, and AFM images, which also confirmed the uniform distribution across the complex surface. Analysis of the absorption patterns for the nano‐sized complexes interaction with CT‐DNA signifies that the complexes bind via intercalation, with intrinsic binding constant (Kb) spanning from 4.25 × 105 to 8.34 × 103 M−1. Nano‐sized metal (II) complexes show a higher viscous nature than HL. The cytotoxicity of H‐L ligand and its complexes against HCT‐116, and M‐NFS‐60 cells was assessed by the MTT assay. Complex C3 shows greater cytotoxic effects compared to Cisplatin on MCF‐7 cells. Our flow cytometry data indicate significant levels of apoptosis and cell cycle arresting in both liver and breast cancer cell lines.

Cross-social-network user alignment research based on multi-dimensional user features

Accurately aligning the same users on different flat social networks to merge user information and create more nuanced user profiles is critical. However, the current research in this area faces challenges related to low efficiency and inadequate alignment accuracy. To address these challenges, we introduce a cross-social network user alignment model based on multi-dimensional user features (MDUF). First, inspired by the principles of entity recognition and Hartley's association method, we employed a block matrix association algorithm to project the original dataset into different high-dimensional spaces. Second, we proposed a new inertia weight calculation method to improve the convergence speed from linear to nonlinear transformations. This method improves the performance of traditional particle swarm optimization algorithms. Finally, we utilize improved particle swarm optimization and residual connection techniques to optimize bidirectional long short-term memory networks. The experimental results show that our proposed model significantly outperforms traditional alignment models in terms of alignment efficiency and accuracy, which is highly practical and has the potential to inspire further research on social network user alignment.

Building energy efficiency evaluation based on fusion weight method and grey clustering method

The renovation and evaluation of building energy-saving projects can provide important support for building an energy-saving society. The study proposes using the contract energy management model to analyze building energy-saving projects and construct an evaluation index system. We also innovatively integrated the Analytic Hierarchy Process and Entropy Weight Method to calculate the weights of indicators, in order to leverage the effective influence of subjective and objective factors. Finally, we used Grey Cluster Analysis to obtain the evaluation effect of building energy-saving projects. Through weight calculation and evaluation analysis, it was found that the energy-saving rates of year-end electricity consumption and air conditioning electricity consumption in buildings after energy-saving renovation were 59.80% and 54.95%, respectively. The overall effectiveness of energy-saving buildings was above 50%, indicating a significant energy-saving effect. In the indicator evaluation system, the weight results of energy-saving service company indicators were relatively high, with values of 0.52, 0.48, and 0.51, respectively. The transformation effect was relatively good. The building energy-saving cost and economic benefits obtained from a 65% energy-saving rate were 3 million yuan and 530,000 yuan, respectively, which were significantly better than the simulation results of other energy-saving rates. The contract energy management model based on the fusion weight method and grey clustering method has superiority, which is effective for evaluating building energy-saving projects. It also provides technical reference and scientific suggestions for building energy-saving renovation.

Algoritma Levenshtein Distance sebagai Solusi Efisiensi Pencarian pada Training Registration System

The training registration process at Company XYZ faced several challenges, including the use of physical documents, slow file submission, non-standardized document numbering, and inefficient file storage. This research aimed to develop a web-based application called "Training Registration System" using the Waterfall software development method to address these issues, as well as implementing the Levenshtein Distance algorithm in the search feature to enhance training search efficiency. The results showed that the application successfully improved the efficiency and accuracy of the training registration and search process by eliminating the need for physical file submission, accelerating the workflow, and reducing the potential for errors. The implementation of the Levenshtein Distance algorithm also proved effective in improving the efficiency of training searches based on training names, even in cases of typos, such as the strings "Trainning" and "Training" which had a similarity score of 87.5% based on similarity weight calculations.

Black Soil Quality After 19 Years of Continuous Conservation Tillage

Conservation tillage is a practice adopted worldwide to prevent soil degradation. Although there have been many studies on the impact of conservation tillage on soil quality, most studies on cultivated land in the black soil region of Northeast China are based on the physical and chemical indicators of soil. In addition, the experiment time is generally short, so there is a lack of information about long-term conservation tillage from the perspective of the physical, chemical, and biological integration of soil. A comparative analysis of the physical, chemical, and biological characteristics of soil was conducted under no-till (NT) with straw mulching and conventional tillage (CT) treatments after 19 years of field experiments. By using membership functions to normalize and render all the indicators dimensionless, and calculating the weight of each indicator through principal component analysis, the comprehensive index of soil quality can be calculated as a weighted summation. The results indicate that NT had no significant effect on soil bulk density at a soil depth of 0–20 cm. NT increased the field water-holding capacity of the 0–5 cm layer, reduced the total porosity of the 5–10 cm soil layer, and decreased the non-capillary porosity of the 0–20 cm soil layer. Compared to CT, NT significantly increased the organic carbon content of the soil in the 0–5 cm layer, comprehensively improved the total nutrient content of the soil, and significantly increased the contents of ammonium nitrogen, nitrate-nitrogen, and available phosphorus in the soil. It also significantly improved the total phosphorus content in the 5–20 cm soil layer. NT improved the microbial carbon and nitrogen content of the soil, significantly enhanced the microbial nitrogen content in the 0–5 and 5–10 cm soil layers, and reduced the bacterial species diversity in the 5–10 cm soil layer. However, the soil enzyme activities showed no significant differences between different treatments. Under the NT treatment, the evaluation of soil quality indicators, such as mean weight diameter, field water-holding capacity, non-capillary porosity, microbial biomass nitrogen, total nutrients, and available nutrients, was relatively successful. Based on the weight calculation, the organic carbon, catalase activity, fungal richness, and bacterial diversity indicators are the most important of the 22 soil quality indicators. In terms of the comprehensive index of soil fertility quality, NT increased the soil quality comprehensive index by 34.2% compared to CT. Long-term conservation tillage improved the physical, chemical, and biological properties of the soil, which significantly enhanced the quality of the black soil.

Evaluating the water quality of the rice–fish co-culture pattern based on the modified NSF water quality index model

The rice–fish co-culture pattern, a sustainable agricultural production method, plays a significant role in enhancing farmland ecological and economic benefits. However, few studies have evaluated the water quality of this system using the water quality index (WQI). In this study, improvements were made to the United States National Sanitation Foundation WQI (NSFWQI), including indicator selection, weight calculation, and sub-indexing, using principal component analysis (PCA), a minimum data set (MDS), linear function equations, and a comprehensive water quality assessment of the double-cropping rice–fish (DRF) co-culture and (DR) rice patterns. The results showed that using PCA; pH, dissolved oxygen (DO), ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), and the Shannon–Wiener index were selected from 11 water quality indicators to construct the MDS for WQI calculation. Based on the WQI results, the DRF had a negative impact on water quality, with the Shannon–Wiener index the greatest contributor to the WQI (22.9 %), consistently ranging between 1 and 3. Although total phosphorus (TP) and chemical oxygen demand (COD) were not included in the MDS for the WQI calculation, their significant increase during the late rice season in the DRF indicated water quality deterioration, further validating the accuracy of the improved NSFWQI. The improved NSFWQI model provides a more objective analysis of water quality under the rice–fish co-culture pattern. Compared with existing water quality evaluation methods, this approach offers advantages such as comprehensiveness, low cost, and standardization, and presents an effective alternative for water quality assessment.