Deviation Method Research Articles

Concurrent Estimation of Pregabalin and Etoricoxib by New Stability-Indicating RP-UPLC Approach - Application in Assay of Commercial Tablets

Background: The study emphasizes establishing a stability-indicating RP-UPLC method for concurrent estimation of pregabalin and etoricoxib in combined pharmaceutical formulations, confirming effective separation, sensitivity, and repeatability, validated according to ICH guidelines. Objective: The present research study aims to establish a new stability-indicating RP-UPLC method for concurrently estimating pregabalin and etoricoxib in blended powder and their combined tablet formulation with less run time, high sensitivity, and specificity. Methods: The effective separation of pregabalin and etoricoxib was achieved with HSS column C18 (150x2.1mm,1.8μm), 0.1% orthophosphoric acid:acetonitrile (65:35 v/v) at a flow rate of 0.3mL/min, and isocratic elution at 228nm. The elution of PRB and ETB was noticed at 1.56 and 2.01 minutes, with good resolution and system suitability with the developed approach. Results: Pregabalin and etoricoxib have shown linear responses from 18.75 to 112.5μg/mL and 15 to 90μg/mL, respectively. The range of the % RSD for intraday and inter-day precision was 0.33 to 0.81. The LOD and LOQ of pregabalin and etoricoxib were computed to be 0.07 μg/mL and 0.21 μg/mL, and 0.01μg/mL and 0.04 μg/mL, respectively, by standard deviation method. The validation method was carried out using ICH standards. The stability-indicating feature of the method was confirmed by the forced degradation studies where degradants generated by stress testing were clearly distinguished from the peaks of analytes. Conclusion: The shorter elution period and superior sensitivity of both analytes with this method have been found to be appropriate for regular analysis of pregabalin and etoricoxib.

A fundamental analysis on PAHs in food products to detect the toxicity index using fuzzy logic system

Polycyclic Aromatic Hydrocarbons (PAHs) are complex chemical compounds that occur naturally in unprocessed food when it is exposed to contaminated air during transportation, natural emission such as volcano, forest fire and through pesticides spray. It is reported by different agencies that there are 16 types of PAHs in which BaP (Benzo[a] pyrene), BaA (Benz[a]anthracene), BbF(Benzo [b] fluoranthene), Chr (Chrysene) are considered to be carcinogenic and it can occur due to different processes. In processed food it occurs due to various processing methods like overheating, incomplete burning, drying etc. The presence of PAH in food is conventionally found through analytical, traditional, and semi-automatic methods. These methods are found to be valuable but expensive and time-consuming. Further, these methods are used only for the detection of PAHs and the toxicity level is measured or identified based on expert knowledge of researchers and the Standards. Therefore, in this research, a simple harmfulness index system has been developed using Fuzzy Logic System(FLS). The proposed system has been designed based on the PAH values of different food and food products. Hence to initiate the study and to determine the significance of the results, PAH data have been collected from different articles that have investigated food products experimentally. These PAH data were analyzed using statistical measures such as Min, Mean, Max, Standard Deviation, Variance and Kurtosis method. Based on the observations from the results, the fuzzy sets were designed with four membership functions for each PAH and the rules were framed. The strength output from the inference engine has been associated with harmfulness index such as normal, low risk, medium risk, and high risk. From the evaluation, it can be observed that 89.72% of the food samples were recognized along with their degree of harmfulness. Also it can be inferred that 11% of the misclassified samples showed clear metrics of their harmfulness with PAH variations.

Technology Leadership of Educational Institution Administrators under the Lopburi Provincial Learning Promotion Office

Background and Aims: Nowadays, technology play an important role in the management of educational institutions. Educational institution administrators need to have technological leadership to be able to manage education effectively. Using existing technology in the management process, promoting and supporting personnel under the organization. It will greatly increase the usefulness and efficiency of work in today's era. Therefore, it is necessary to develop, improve and modernize the existing education system. and consistent with technological changes this research has the following objectives: (1) Study the level of technological leadership of educational institution administrators under the Lopburi Provincial Learning Promotion Office. (2) Compare the technological leadership of educational institution administrators under the Lopburi Provincial Learning Promotion Office classified by position education level and work experience. (3) Study guidelines for developing technological leadership of educational institution administrators under the Lopburi Provincial Learning Promotion Office. Methodology: The sample group used in the research included 127 school administrators and teachers under the Lopburi Provincial Learning Promotion Office. The sample size was determined according to Krejci and Morgan's table. and the sample was selected using stratified random sampling. The research instrument consisted of a questionnaire using a 5-level rating scale with a consistency index between 0.80-1.00 and the reliability of the entire questionnaire was 0.96. In addition, interviews were used as additional tools. In data analysis use a variety of statistics including frequency, percentage, mean, standard deviation, T-test, F-test, and Scheffe's Method analysis. Results: The results of the research found that: (1) Technology leadership of school administrators under the Lopburi Provincial Learning Promotion Office overall and in each aspect, it is at a "very" level. (2) Technology leadership comparison results of administrators of educational institutions under the Lopburi Provincial Learning Promotion Office classified by position Education level and work experience It was found that overall and, in each aspect, there were no differences. However, when classified according to work experience It was found that overall, there were no differences. But in terms of supporting the use of technology in measurement and evaluation. There is a statistically significant difference at the 0.05 level. (3) Guidelines for developing technology leadership of educational institution administrators under the Lopburi Provincial Learning Promotion Office, it consists of 5 main areas: 1) Having leadership and technology vision, 2) supporting teaching and learning using technology, 3) ethics in using technology, 4) supporting the use of technology in administration, 5) supporting the use of technology in measurement and evaluate. Conclusion: Technology leadership of educational institution administrators’ emphasis should be placed on appropriate management and application of technology in educational institutions. To achieve maximum efficiency in operations and development of educational quality.

How much is too much: A case study of local self-government units in Slovakia using absolute variability to determine the importance of financial criteria in MCDM analysis.

The performance evaluation of local self-government entities is very difficult, as their primary goal is not to make a profit, but to provide services to their residents that will contribute to an increase in their quality of life. In this context, it is necessary to evaluate their activity from the point of view of several available criteria, for which it is possible to find relevant and recognized sources. The presented research works with five criteria, identified by the Institute for Economic and Social Reforms, and aims to quantify the agreement of the results of the assessment of the financial health of territorial self-government entities in 2020 using the TOPSIS technique with a gradually decreasing number of criteria. For this purpose, a total of 26 combinations of criteria are created, with the number of 5, 4, 3 and 2 used criteria, the importance of which is determined based on their absolute variability using the standard deviation method. The results obtained in this way are interpreted using a wide range of mathematical and statistical methods including the Kolmogorov-Smirnov test, Levene test, Jaccard index and others. As a result, the multi-criteria evaluation of territorial self-government subjects (in our case, district cities) proved to be highly applicable. However, the result itself is largely determined by the structure and number of entry criteria. Based on the performed analyses, we can see that significant differences result from their reduction. Each such reduction has an impact on the overall results, but it is possible to find combinations that defy this conclusion.

Urban nighttime aerosol optical depth retrieval methods from SDGSAT-1 glimmer data

ABSTRACT Aerosol Optical Depth (AOD) is vital for assessing air quality and climate. Nighttime lights (NTL) provide a new data source for urban AOD inversion, but current NTL data suffer from low spatial resolution, limiting detailed AOD studies in smaller urban areas and hindering high-precision AOD algorithms. This study introduces methods for retrieving AOD using Glimmer Imager (GLI) data from the newly launched SDGSAT-1. Three methods were proposed: the standard deviation method, look-up-table method, and collaborative inversion method with VIIRS Day Night Band (DNB) data. Applied to Beijing, these methods produced a 10 m resolution AOD product. The look-up-table method achieved the highest correlation (R = 0.8876) compared to the standard deviation (R = 0.8224) and collaborative inversion (R = 0.8322) methods. The look-up-table method performed best with ample data, while the collaborative inversion method proved more reliable with limited data. These findings demonstrate the effectiveness of SDGSAT-1 GLI data in accurately retrieving nighttime AOD, offering significant potential for monitoring urban pollution.

Novel decision making approach for sustainable renewable energy resources with cloud fuzzy numbers

Decision making approaches depending on the assessments of individual decision makers produce inaccurate results due to the existence of multiple uncertainties. To model intrapersonal uncertainty, interpersonal uncertainty and randomness in decision making assessments, this research study proposes a novel approach by integrating linear and non-linear type of fuzzy numbers with cloud model theory using novel technique of computing entropy of these fuzzy numbers. A novel mathematical model known as cloud fuzzy numbers is introduced using the concepts of fuzzy numbers and cloud theory. The self-evaluated relative weights of experts are computed using a non-linear optimization method which is based on maximum deviation method and Lagrange multipliers of cloud fuzzy numbers. The new cloud fuzzy numbers are then combined with CODAS (combinative distance based assessment) approach that is based on the largest Euclidean and Taxicab distances for the selection of suitable criteria. Firstly, the linguistics evaluations are converted into the fuzzy numbers and then cloud fuzzy numbers using formulae of expectation and entropy ensuring that the obtained interval cloud values follows a normal distribution. Secondly, the cloud fuzzy weighted arithmetic averaging operator is used to aggregate cloud fuzzy numbers using the self-evaluated fuzzy weights Thirdly, the assessment score is determined to rank the alternatives by computing the distance between normalized weighted matrix and the negative ideal solution. Finally, a case study is discussed for the selection of best renewable energy resource in Turkey to elaborate the significance of the proposed research. The convergence and accuracy of the proposed model is proved with certain mathematical and theoretical results.

An enhanced CRADIS decision model for optimizing radioactive waste reduction through transmutations based on Disc Spherical Fuzzy information

Radioactive waste reduction through transmutation techniques is significant for mitigating environmental risks and ensuring long-term safety. It offers a sustainable approach to minimizing the volume of radioactive waste, contributing to both environmental preservation and enhanced nuclear energy sustainability. This paper introduces an extension of aggregation operations to hybrid operators in Disc Spherical Fuzzy Sets (D-SFSs), including D-SFS algebraic hybrid weighted averaging and D-SFS hybrid geometric aggregation operators. These novel operators are tailored for D-SFSs and are supported by rigorous proofs, enhancing their theoretical robustness. Additionally, a Maximum Deviation Method (MDM) is developed in conjunction with a proposed distance measure to assess attribute weights in Multiple Attribute Decision-Making (MADM) problems with Disc Spherical Fuzzy (D-SF) information. The study proposes a new MADM method in the D-SF environment, leveraging the D-SF distance measure and the Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) method. Notably, the paper focuses on extending D-SFs to the CRADIS method, particularly when weight information is entirely unknown. A comprehensive case study on optimizing radioactive waste volume reduction through transmutation technologies is provided. A comparative analysis with existing decision-making methods validates the improved MADM method’s validity and practicality.

Ecological flow research in response to hydrological variation: A case study of the Jinsha River Basin, China

Climate change and human activities have altered the natural state of rivers to a certain extent, leading to runoff variation and impacting the stability of riverine ecosystems. The ecological flow has emerged to balance the water relationship between humanity and riverine ecosystems and to safeguard the health and biodiversity of rivers. Presently, the scientific quantification of riverine ecological flow has become a focal point in water resource research. Addressing the quantification of ecological flow under hydrological variation conditions, the cumulative deviation method (CDM) and Pettitt methods were firstly employed to detect breakpoints in the runoff sequences. Then, the natural monthly runoff post-variation was restored using the Soil and Water Assessment Tool (SWAT) model, i.e., natural runoff restoration, and three testing methods were employed to determine the optimal hydrological distribution functions for natural monthly runoff. Subsequently, an improved frequency calculation method was used to calculate the ecological flow. The Jinsha River Basin was selected as the study area, and four crucial sections in the mainstream of the Jinsha River were studied, including Zhimenda (ZMD), Shigu (SG), Xiaodeshi (XDS), and Pingshan (PS). The results indicated: (1) the runoff sequences (1960–2017) at the four crucial sections exhibit different breakpoint times, with breakpoint years being 2004, 1986, 2005, and 1996, respectively; (2) the SWAT model, in simulating runoff at the four crucial sections, exhibited NSE values all exceeding 0.85, and r values all surpassing 0.9, demonstrating its effective ability to restore natural runoff in the Jinsha River basin; (3) the optimal hydrological distribution functions for natural monthly runoff at the four sections varied, and most natural monthly runoff conforms to the Generalized Extreme Value (GEV) distribution; (4) the average annual ecological flows for the ZMD, SG, XDS, and PS sections were 296 m3/s, 992 m3/s, 1238 m3/s, and 2755 m3/s, and the ecological flows derived using the improved frequency calculation method under the optimal hydrological distribution functions were more rational. This study can provide a scientific basis for the optimal allocation of water resources and the protection of riverine ecosystems in the Jinsha River basin.

Exploring the Identification of Autoregression Model by General Least Deviation Method

Exploring the Identification of Autoregression Model by General Least Deviation Method

Geochemical baseline establishment and accumulation characteristics of soil heavy metals in Sabaochaqu watershed at the source of Yangtze River, Qinghai-Tibet Plateau

The establishment of soil geochemical baseline and heavy metal pollution assessment in the Qinghai-Tibet Plateau is of great significance for guiding environmental management in the high-cold and high-altitude regions. A total of 126 topsoil samples (0–20 cm) were collected and the contents of Cu, Pb, Zn, Ni, Cr, Cd, As and Hg were determined in the Sabaochaqu basin of the Tuotuo River, the source of the Yangtze River, in the Tibetan Plateau. The baseline values of 8 heavy metals were determined by mathematical statistics, iterative 2times standard deviation method, cumulative frequency and reference element standardization, and the soil heavy metal pollution in the study area was assessed by enrichment factor method and pollution index method. The results showed that the average contents of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn were 31.84, 0.29, 66.07, 17.35, 0.021, 27.86, 49.35 and 88.56 mg/kg, respectively. Baseline values were 22.24, 0.217, 64.16, 15.69, 0.0191, 26.46, 34.91, and 68.62 mg/kg, respectively. There is a great difference between the baseline value of soil heavy metals in study area and the Xizang soil background value, especially the baseline value of Cd was 2.68 times of its background value. The results of the pollution evaluation based on the baseline values showed that the 8 heavy metals were slightly enriched, and the overall pollution status was light pollution, and measures should be taken to control and manage them. The research results can provide a reference value for the evaluation of soil heavy metal pollution in the source region of the Yangtze River, and also provide a theoretical basis for the construction of soil heavy metal baseline values in similar high-cold and high-altitude regions.

Precipitation measurements experiment using microwave links in Eastern China from October 2020 to March 2022

Precipitation is an important piece of information needed in many areas such as transportation, military and agriculture, and microwave links have proven to be an effective means of acquiring it and can be used as a complementary means for professional precipitation measurement instruments such as rain gauges, weather radar, rain measuring satellites, etc. In this paper, two microwave links (at 26 GHz, both vertically polarised) located across a river in eastern China and a nearby OTT PARSIVEL disdrometer were used to carry out a precipitation observation experiment from October 2020 to March 2022 (March to November 2021 for liquid precipitation, others for possible non-liquid precipitation). The applicability of the dry and rainy period identification methods (standard deviation method and correlation coefficient method) based on this link data is analyzed first. In addition, a wet antenna attenuation correction model based on a Long Short-Term Memory (LSTM) neural network was applied to non-winter precipitation inversion. The results show that the rain rate and cumulative rainfall calculated from the two links are in good agreement with that calculated by the disdrometer data. Furthermore, quantitative inversion of winter precipitation using two microwave links is performed, and the variation characteristics of the link levels during non-liquid precipitation periods are also analyzed.

Online detection of mixed oil interface in multi-product pipeline using near-infrared spectroscopy

To ensure the quality of refined oil products of sequential transportation, it is necessary to monitor and judge the interfaces between pure oils and mixed oil for guiding accurate mixed oil cutting. In this study, near-infrared spectroscopy (NIRS) was used to investigate the change trend of oil concentration and thus judge the interfaces. The methods of standard deviation of continuous spectra, principal component analysis, difference spectrum, and vector angle θ were used to analyze the online oil NIR spectra to detect the interfaces, respectively. The results show that θ of the spectra pretreated by the combination of airPLS and 1st derivative is the best for reflecting the concentration change trend of mixed oil and judging the interfaces. The interface between the forward pure oil and mixed oil can be judged by a fixed θ threshold, and the interface between the rear pure oil and mixed oil can be judged by a fixed first-order derivative value of the five-parameter logistic fitted curve of θ. This method is not affected by the interference and oil composition change of different batches and does not require oil properties and pipeline parameters, thus it has a good prospect in the oil pipeline transportation field.

Archimedean [formula omitted]-norm and [formula omitted]-conorm coupled q-rung orthopair fuzzy TOPSIS method for unknown criteria weighting information

To evaluate some real-life multi-criteria decision-making (MCDM) problems, the decision-makers (DMs) need an interactive and flexible MCDM approach that works well for extended decision-making space and has the ability to apply any suitable operational laws to fuse the information under incomplete criteria weighting information. The objective of this study is to propose a multi-criteria group decision-making (MCGDM) approach to evaluate any real-life MCDM problem under incomplete criteria weighting information. To achieve the objective, the paper proposes an integrated MCGDM approach in which the q-rung orthopair fuzzy set (q-ROFS) is used to provide the extended decision-making space to the DMs, a transformative approach is applied to find the DMs’ weights, the maximizing deviation method is implemented to find the criteria weights, Archimedean t-norm and t-conorm (ATU)-based operational laws are adopted to fuse the information, and the TOPSIS method is applied to rank the alternatives. The proposed integrated MCGDM approach has been applied to a real-life MCDM problem associated with the evaluation of different strategies implemented by governments to contain the COVID-19 pandemic. A sensitivity analysis is conducted to examine the impact of changes in the parameters involved in the proposed MCGDM approach on the results. A comparative analysis is also performed with some existing classical and aggregation operator-based MCDM methods to demonstrate the consistency of the proposed approach. Findings suggest that the proposed MCGDM approach is general and flexible enough to evaluate any real-life MCDM problem under unknown criteria weighting conditions.

Soft Computing-Based Generalized Least Deviation Method Algorithm for Modeling and Forecasting COVID-19 using Quasilinear Recurrence Equations

Soft Computing-Based Generalized Least Deviation Method Algorithm for Modeling and Forecasting COVID-19 using Quasilinear Recurrence Equations

Epidemiological patterns of natural focal diseases of the Voronezh region

Diseases of animals and humans are widespread and are confined to the habitat of the reservoir. Urbanization has led to the expansion of the range of natural focal diseases with the formation of persistent anthropurgic foci.The purpose of the work was to study the epizootic manifestations of natural focal diseases and analyze the morbidity of the population of the Voronezh region. The research material was based on data from federal statistical observation forms No. 2 for 2012–2022, and the results of epizootological monitoring of the department of natural focal infections and laboratories of the Center for Hygiene and Epidemiology in the Voronezh region. A retrospective epidemiological analysis of the morbidity rate of the population of the Voronezh region was carried out, ranking administrative territories based on the method of standard deviations. The results obtained were processed using Microsoft Excel; the long-term average incidence rate, error of the mean, confidence intervals, and significance level (p) less than 0.05 were calculated. The result of the analysis showed that the long-term average incidence rate of natural focal diseases in the Voronezh region was 9.91 per 100,000 population. In the structure of nosological forms, 85.98 % were tick-borne borreliosis and hemorrhagic fever with renal syndrome. The long-term average incidence rate of Lyme disease is 4.45 per 100,000 population, hemorrhagic fever with renal syndrome is 0.87 per 100,000 population. According to the ranking results, the Ramonsky, Ternovsky and Anninsky administrative territories were classified as risk areas for the average long-term incidence of borreliosis, and the Ramonsky, Verkhnekhava and Paninsky administrative territories for hemorrhagic fever with renal syndrome. The results obtained indicate active natural foci of infections in the Voronezh region.

Perbandingan Value at Risk dan Expected Shortfall pada Portofolio Optimal menggunakan Metode Downside Deviation

Portfolio formation is one of the strategies that investors can do to get the best results Portfolio formation can use the Downside Deviation method. The optimal portfolio with this method uses downside deviation and sets the return below the benchmark as a measure of risk. Every optimal portfolio certainly cannot be separated from risk. To measure risk, you can use the Value at Risk (VaR) and Expected Shortfall values. This study aims to form an optimal portfolio using the Downside Deviation method and continued by comparing the possible losses that occur from the formed portfolio using the VaR and Expected Shortfall values. The data used in this study is the daily closing price data of LQ-45 Index stocks in the banking sector in the period February-June 2023. From the stock data, data selection is carried out by selecting stocks that have a positive expected return and are normally distributed. Then, the optimal portfolio formation stage is continued using the Downside Deviation method and comparing the possible risks formed with the VaR and Expected Shortfall values. The results of this study show that the optimal portfolio with the Downside Deviation method consists of four stocks, namely with the stock codes BRIS.JK, BBRI.JK, BBNI.JK, and BBCA.JK. This study uses a case example by investing capital of Rp100,000,000 with a one-day time period and three levels of confidence, namely 90%, 95%, and 99%. Based on the comparison of the risk value of the portfolio formed using VaR and Expected Shortfall, it is shown that the possible risk with the Expected Shortfall method is greater than the VaR value. Therefore, Expected Shortfall is better in estimating the maximum risk.

ANALYSIS OF OPTIMAL PORTFOLIO FORMATION ON IDX30 INDEXED STOCK WITH THE MEAN ABSOLUTE DEVIATION METHOD

In investing in stocks, an investor must be able to form a stock portfolio to obtain optimal results. Factor analysis is one way to select stocks to form a portfolio. Factor analysis with Principal Component Analysis (PCA) extraction is used to summarize many variables into new smaller factors by producing the same information. The new factor formed is called a portfolio. This study aims to form an optimal portfolio using the Mean Absolute Deviation (MAD) method, which is an alternative to Markowitz optimization, and assess the stock portfolio's performance using the Sharpe index. This research uses IDX30-indexed stocks because the stocks in this index have high market capitalization and liquidity. The data used in this study are daily close stock price data on the IDX30 index from September 20, 2022, to September 20, 2023. The data used is secondary data obtained from the official website https://finance.yahoo.com/. From the analysis, three stock portfolios were obtained. With MAD optimization, the investment weight of each stock is obtained namely, in the first portfolio, the investment weight of AMRT shares is 21.95%, BBCA shares are 30%, BBNI shares are 18.05%, and BBRI shares are 30%. In the second portfolio, the investment weight of AKRA shares is 34.03%, BRPT shares are 40%, and MEDC shares are 25.97%. In the third portfolio, the investment weight of BMRI shares is 50%, and INDF shares are 50%. By measuring the performance of the Sharpe index, the optimal portfolio is obtained in the second portfolio with an expected return portfolio of 0.155% and a portfolio risk of 1.927%.

Effect of dielectric material on the uniformity of nanosecond pulsed dielectric barrier discharge

Dielectric barrier discharge (DBD) is considered as a promising technique to produce large volume uniform plasma at atmospheric pressure, and the dielectric barrier layer between the electrodes plays a key role in the DBD processes and enhancing discharge uniformity. In this work, the uniformity and discharge characteristics of the nanosecond (ns) pulsed DBD with dielectric barrier layers made of alumina, quartz glass, polycarbonate (PC), and polypropylene (PP) are investigated via discharge image observation, voltage-current waveform measurement and optical emission spectral diagnosis. Through analyzing discharge image by gray value standard deviation method, the discharge uniformity is quantitatively calculated. The effects of the space electric field intensity, the electron density (N e), and the space reactive species on the uniformity are studied with quantifying the gap voltage U g and the discharge current I g, analyzing the recorded optical emission spectra, and simulating the temporal distribution of N e with a one-dimensional fluid model. It is found that as the relative permittivity of the dielectric materials increases, the space electric field intensity is enhanced, which results in a higher N e and electron temperature (T e). Therefore, an appropriate value of space electric field intensity can promote electron avalanches, resulting in uniform and stable plasma by the merging of electron avalanches. However, an excessive value of space electric field intensity leads to the aggregation of space charges and the distortion of the space electric field, which reduce the discharge uniformity. The surface roughness and the surface charge decay are measured to explain the influences of the surface properties and the second electron emission on the discharge uniformity. The results in this work give a comprehensive understanding of the effect of the dielectric materials on the DBD uniformity, and contribute to the selection of dielectric materials for DBD reactor and the realization of atmospheric pressure uniform, stable, and reactive plasma sources.

Study on the Driving Mechanism of Ecohydrological Regime in the Wandering Section of the Lower Yellow River

Climate change and human activities exert significant impacts on runoff generation and convergence mechanisms. Understanding the evolution mechanisms and driving forces of runoff is crucial for the sustainable development of water resources. This study is based on the runoff data of the Huayuankou (HYK), Jiahetan (JHT), and Gaocun (GC) stations in the lower reaches of the Yellow River from 1951 to 2019. The objectives are to identify and quantify the hydrological pattern and its driving mechanism of the three stations by the Mann–Kendall test, cumulative deviation method, wavelet analysis, the IHA-RVA method, SCRCO method, and the Budyko method. Our study revealed that the runoff disturbance points at all three stations occurred in 1985. During the two periods before and after the disturbance, the trends and periodicities within the year exhibited high consistency, showing an overall downward trend, with runoff increasing in October and decreasing in June and the primary cycles being 5 and 7 years. All three stations experienced high-degree changes in their hydrological situations, with the high-degree change occupying the largest proportion. At the HYK, JHT, and GC stations, human activities accounted for 66.05%, 71.94%, and 74.38% of runoff changes, respectively. Furthermore, we verified the attribution conclusion of runoff at HYK using the Budyko model, confirming that human activities are the primary factor influencing runoff. Finally, we explored the interactive relationships along the spatial trajectory of runoff at the three stations, analyzed 32 hydrological indicators, and detailed the land use changes in the Yellow River Basin. Our research findings complement the understanding of hydrological change mechanisms in the lower reaches of the Yellow River Basin and provide a scientific basis for future water resource management and flood prevention measures.

Prioritization of sustainable development plannings for supply chain finance using a novel Triangular neutrosophic-based TOPSIS-CPT-VIKOR model

The development of sustainable supply chain finance (SSCF) promotes the improvement of the long-term production and operational performance of enterprises. It is significant to explore both environmentally friendly and cost-effective solutions, especially in the presence of uncertain information. The triangular neutrosophic number (TrNN) can enable decision-makers (DMs) to express their preferences in high uncertainty. This paper aims to provide a systematic multiple-attribute group decision-making (MAGDM) framework to support DMs in prioritizing sustainable development plannings by combining the best-worst method (BWM), optimization model of maximizing deviation method, and uniformity entropy theory. Moreover, a TrNN-based extended TOPSIS method is developed to measure the differences in DMs’ importance. We incorporate the cumulative prospect theory (CPT) method to account for DMs’ psychological behaviors in risk scenarios. The CPT-VIKOR methodology is employed to rank alternatives, enhancing the precision of decision-making. Finally, the proposed approach is successfully applied to the LNG industry to illustrate its applicability.