Ranking Method Research Articles

Study on Cost and Returns of Cash Crops in Sirmour District, Himachal Pradesh, India

The present study examines the cost and returns of major cash crops raised by different category of farmers in district Sirmour, Himachal Pradesh. The study also identified the different production problems faced by farmers using weighted rank method. Two stage stratified random sampling technique was followed to select a sample of villages and the ultimate farmers. A random sample of 60 farmers was chosen for the study. The major findings revealed that production of vegetables varied from 6.42 qtl for peas to as high as 25.93 qtl of tomato per farm. Garlic being a spice crop recorded the production of 14.37 quintal per farm. In the study, per hectare cost of cultivation was found to be highest for tomato, i.e., Rs. 34200, followed by garlic (Rs. 26134) and beans (Rs. 15427) and lowest for peas (Rs. 11247), The per hectare net return was found to be higher for garlic crop (Rs.115066) when compared to tomato crop (Rs. 55785), beans and peas (Rs. 17108 to Rs. 6703). Therefore, garlic crop was found to be the most profitable crop with the net return of Rs. 115066 which was more and double of profit earned from tomato (Rs. 55785), peas (Rs. 6703) and beans (Rs. 17108). Similarly, the cost of cultivation was found to be highest for the large farmers and the per hectare net return also seemed to be the highest for the large farmers. Likewise, gross returns were also found highest for large farmers than that of small farmers. Further, the study revealed that major production problems faced by the farmers were availability of quality seed, high cost of inputs which were rated at top, followed by monkey & stray animals, quality of seed not available, high cost of inputs and high incidence of pest and pesticides etc. In order to increasing farm production and to minimise operational costs, the availability of high yielding variety seeds, reasonable prices of inputs like seed, fertilizers, pesticides etc., may enhance the farmers’ net return and income.

Comparison of short-to medium-term ischemia and bleeding risks between unfractionated heparin and bivalirudin in patients with acute coronary syndrome after PCI

Objective: To compare the short-and medium-term ischemia and bleeding risk between unfractionated heparin and bivalirudin in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI). Methods: A total of 742 patients with ACS who underwent emergency PCI in Xinxiang Central Hospital of Henan Province from January 2016 to June 2022 were selected and divided into unfractionated heparin group (385 cases) and bivalirudin group (357 cases) according to the anticoagulant regimen. All patients were followed up for 6 months. The incidence of ischemic and bleeding events at 30 days and 6 months after operation were compared between the two groups. Multivariate Cox proportional regression model was used to analyze the risk factors of ischemic and bleeding events in the two groups. Kaplan Meier method was used to calculate the cumulative survival rate, and log rank method was used to analyze the difference in survival rates. Results: The age of 742 patients was (62.5±14.8) years old, and male accounted for 58.5% (434 cases). The age of unfractionated heparin group was (61.8±14.8) years old, and male accounted for 59.2% (228 cases); The age of bivalirudin group was (63.3±14.8) years old, and male accounted for 57.7% (206 cases). The incidence of bleeding events at 30 days and 6 months in the unfractionated heparin group were 6.8% (26 cases) and 9.9% (38 cases), respectively, which were higher than 3.4% (12 cases) and 4.5% (16 cases) in the bivalirudin group (all P0.05); The incidence of ischemic events at 30 days and 6 months in the unfractionated heparin group were 7.5% (29 cases) and 11.2% (43 cases), respectively, which were not observed to be significantly different with those in the bivalirudin group [6.2% (22 cases) and 9.5% (34 cases)] (all P0.05). Compared with patients using bivalirudin, the HR value (95%CI) of bleeding events after emergency PCI in patients using unfractionated heparin was 1.964 (1.317-3.125) (P0.05), and the HR value (95%CI) of ischemic events was 0.948(0.595-1.510) (P0.05). The cumulative incidence of bleeding events was 9.9% in unfractionated heparin group and 4.5% in bivalirudin group (P=0.005); The cumulative incidence of ischemic events was 11.2% in unfractionated heparin group and 9.5% in bivalirudin group (P=0.459). Conclusions: The incidence of short-term hemorrhage events in ACS patients treated with bivalirudin anticoagulation after emergency PCI is lower than that of unfractonated heparin, which can reduce the risk of short-term hemorrhage.

On the issue of strengthening the food security of the regions by stimulating import substitution

The relevance of the research topic is due to the fact that food security is the most important factor in preserving the statehood and sovereignty of the country, one of the main structural units of socio-economic policy, a necessary condition for the implementation of the strategic priorities of the state, which consist in significantly improving the quality of life of the Russian population through guaranteed living standards.The purpose is to analyze the state policy to stimulate import substitution in the field of strengthening food security based on the assessment of the current state of food security in Russia.Objectives: to characterize the structure, criteria and levels of food security of the regions and the state as a whole; to assess the current state of food security in Russia; to identify the problems of ensuring food security associated with the risks and barriers to the implementation of Russia's food policy; to consider the directions of import substitution as a mechanism for strengthening the food security of the regions and the state.Methodology. In the processing of factual material, traditional scientific approaches were used – dialectical and systemic. The methods of comparison, statistical analysis and ranking were used as research tools.Results. Based on the assessment of the state of food security in Russia in dynamics, it is proved that the government of the Russian Federation has set the regions the task of ensuring the country's food security in advance, before the start of the implementation of the package sanctions policy of the West. This made it possible to implement some areas of import substitution in the agro-industrial complex in the first place.Conclusions. However, the volume of sanctions restrictions that began against Russia in February 2022 again raised the question of strengthening the country's food security. The government began to introduce various financial, logistical and other mechanisms to strengthen food security, but the greatest emphasis was placed on the continuation and deepening of the import substitution policy.

Labor Engine or Welfare Trap: New Evidence from the Impact of China's Long-Term Care Insurance Policies on Labor Market Performance

The long-term care insurance system is a crucial social service policy for disabled and elderly individuals with dementia in the context of the unfeasible traditional family old-age care model. Through the support of the social insurance model for old-age care security, it effectively addresses and meets the actual needs of long-term care recipients. This has become a significant way to alleviate the burden of disabled and demented elderly individuals, mitigate the impacts of ageing, and create employment opportunities for family caregivers. Using the long-term care insurance pilot policy implemented in Qingdao in 2012 as a quasi-natural experiment, this paper estimates the influence of long-term care insurance on the labor market performance of the pilot city using the synthetic control method of project policy evaluation. Several robustness tests of the labor market effects of long-term care insurance are conducted, utilizing two placebo methods based on time and space, and the ranked test method. The study's findings demonstrate that the implementation of the long-term care insurance policy pilot has a constructive influence on the employment choices of workers in the labor market. Specifically, it encourages participation in the workforce and expands the scale of labor force participation in the pilot cities. Furthermore, the policy's effect strengthens over time and has a marked positive effect on average employee wages, thereby spurring labor supply growth. Through further analysis, it can be determined that the pilot cities have contributed to an average annual growth of around 1 million people in total labor force participation between 2013-2017, surpassing the potential growth rate by approximately 10.5%. Additionally, during this period, the average employee wage displays a linear upward trend with a maximum increase of over 5,000 pounds per capita per year. This suggests a substantial effect on the overall performance of the labor market due to its growth-promoting influence. In addition, the analysis of the heterogeneity of its impact mechanism demonstrates that the long-term care insurance policy aims to enhance the willingness of tertiary industry workers to work and considerably increase the likelihood of their entry into the labor market. It also improves labor income expectations of urban private and self-employed workers, resulting in a virtuous circle of enhanced labor and employment driven by the increased value of labor and the strengthened willingness to participate.

Neutrosophic fuzzy decision-making framework for site selection

These days, the area of interest based on canteen location selection in the university campus is a prominent topic. This research work directly affects students' convenience, accessibility and the total campus experience. An ideal canteen site not only provides good food to the students and staff but also takes care of their health by providing a healthy environment. In this paper, we choose various criteria and sub-criteria to calculate the best site for a canteen in the university campus. Location, space and layout, infrastructure and Utilities, safety and compliance and environmental factors as criteria with their corresponding sub-criteria are chosen for this work. Here, the data sets used are provided by decision makers in linguistic words and then converted into crisp numbers. The criteria and sub-criteria weights are considered by a popular multi criteria decision-making (MCDM) method named Criteria Importance Through Intercriteria Correlation, i.e., CRITIC method. Therefore, the ideal location for the canteen in the university campus was determined by applying the complex proportional assessment method (COPRAS), which is basically a ranking method of MCDM. And finally, sensitivity analysis was conducted to make sure the outcomes were flexible and stable.

The seventh blind test highlights exciting developments in crystal structure prediction.

Two reports on the seventh blind test on crystal structure prediction extensively discuss the cutting-edge avant-garde methods of structure generation and energy ranking.

An effective surrogate-assisted rank method for evolutionary neural architecture search

Evolutionary neural architecture search (ENAS) is able to automatically design high-performed architectures of deep neural networks (DNNs) for specific tasks. In recent years, surrogate models have gained significant traction because they can estimate the performance of neural architectures, avoiding excessive computational costs for training. However, most existing surrogate models primarily predict the performance of architectures directly or predict pairwise comparison relationships, which makes it challenging to obtain the rank of a group of architectures when training samples are limited. To address this problem, we propose TCMR-ENAS, an effective triple-competition model-assisted rank method for ENAS. TCMR-ENAS employs a novel triple-competition surrogate model combined with a score-based fitness evaluation method to predict group performance rank. Moreover, a progressive online learning method is proposed to enhance the predictive performance of the triple-competition surrogate model in the framework of modified genetic search. To validate the effectiveness of TCMR-ENAS, we conducted a series of experiments on NAS-Bench-101, NAS-Bench-201, NATS-Bench and NAS-Bench-301, respectively. Experimental results show that TCMR-ENAS can achieve better performance with lower computational resources. The accuracies of searched architectures achieve the best results compared with those of the state-of-the-art methods with limited training samples. In addition, the factors that may influence the effectiveness of TCMR-ENAS are explored in the ablation studies.

Ferumoxytol-Enhanced Cardiac Cine MRI Reconstruction Using a Variable-Splitting Spatiotemporal Network.

Balanced steady-state free precession (bSSFP) imaging is commonly used in cardiac cine MRI but prone to image artifacts. Ferumoxytol-enhanced (FE) gradient echo (GRE) has been proposed as an alternative. Utilizing the abundance of bSSFP images to develop a computationally efficient network that is applicable to FE GRE cine would benefit future network development. To develop a variable-splitting spatiotemporal network (VSNet) for image reconstruction, trained on bSSFP cine images and applicable to FE GRE cine images. Retrospective and prospective. 41 patients (26 female, 53 ± 19 y/o) for network training, 31 patients (19 female, 49 ± 17 y/o) and 5 healthy subjects (5 female, 30 ± 7 y/o) for testing. 1.5T and 3T, bSSFP and GRE. VSNet was compared to VSNet with total variation loss, compressed sensing and low rank methods for 14× accelerated data. The GRAPPA×2/×3 images served as the reference. Peak signal-to-noise-ratio (PSNR), structural similarity index (SSIM), left ventricular (LV) and right ventricular (RV) end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF) were measured. Qualitative image ranking and scoring were independently performed by three readers. Latent scores were calculated based on scores of each method relative to the reference. Linear mixed-effects regression, Tukey method, Fleiss' Kappa, Bland-Altman analysis, and Bayesian categorical cumulative probit model. A P-value <0.05 was considered statistically significant. VSNet achieved significantly higher PSNR (32.7 ± 0.2), SSIM (0.880 ± 0.004), rank (2.14 ± 0.06), and latent scores (-1.72 ± 0.22) compared to other methods (rank >2.90, latent score < -2.63). Fleiss' Kappa was 0.52 for scoring and 0.61 for ranking. VSNet showed no significantly different LV and RV ESV (P = 0.938) and EF (P = 0.143) measurements, but statistically significant different (2.62 mL) EDV measurements compared to the reference. VSNet produced the highest image quality and the most accurate functional measurements for FE GRE cine images among the tested 14× accelerated reconstruction methods. 3 TECHNICAL EFFICACY: Stage 1.

Analysis of the Impact of the Social Environment on the Implementation of the Construction of the Wiego Warehouse Building on Jalan Letda Sujono Medan

In modern civilization, the construction of modern shopping centers is often a symbol of a city's progress. The implementation of development will definitely have various impacts, one of which is social and environmental impacts. This research aims to determine the social and environmental impact of the construction of the Wiego Warehouse Building on Jalan Letda Sujono. This research was carried out by distributing questionnaires to the local community during the construction of the project. The data collection technique used a questionnaire by distributing it to 71 respondents with question criteria of social impact and environmental impact. The research method used is descriptive analysis, validity testing and reliability testing. The results of the social and environmental impact validity test have a calculated r value greater than the r table, namely 0.2303, and the social impact reliability test results show a Cronbach Alpha value (0.935) 0.7, while the environmental impact Cronbach Alpha (0.974) 0, 7. The ranking method is used to determine the ranking of respondents and give priority to study variables, after collecting data from respondents, it is then analyzed using the Mean value, which is a group explanation technique based on the average value to get the Mean value, processing questionnaire data using the SPSS program with descriptive analysis methods. From the research results, a ranking order for each social and environmental impact was obtained. The most important social impact is disruption of traffic comfort, and the most important environmental impact is the effect of implementing this project on the emergence of air pollution.

Carbon Emission Trend Prediction for Regional Cities in Jiangsu Province Based on the Random Forest Model

This study accounted for and analyzed the carbon emissions of 13 cities in Jiangsu Province from 1999 to 2021. We compared the simulation effects of four models—STIRPAT, random forest, extreme gradient boosting, and support vector regression—on carbon emissions and performed model optimization. The random forest model demonstrated the best simulation performance. Using this model, we predicted the carbon emission paths for the 13 cities in Jiangsu Province under various scenarios from 2022 to 2040. The results show that Xuzhou has already achieved its peak carbon target. Under the high-speed development scenario, half of the cities can achieve their peak carbon target, while the remaining cities face significant challenges in reaching their peak carbon target. To further understand the factors influencing carbon emissions, we used the machine learning interpretation method SHAP and the features importance ranking method. Our analysis indicates that electricity consumption, population size, and energy intensity have a greater influence on overall carbon emissions, with electricity consumption being the most influential variable, although the importance of the factors varies considerably across different regions. Results suggest the need to tailor carbon reduction measures to the differences between cities and develop more accurate forecasting models.

Speech Signal Analysis in Patients with Parkinson’s Disease, Taking into Account Phonation, Articulation, and Prosody of Speech

This study involved performing tests to detect Parkinson’s disease (PD) based on voice changes, including speech phonation, articulation, and prosody, in patients with PD using different types of speech signal. For this purpose, during the first stage of the investigation, three separately modeled PD diagnosis systems using different types of speech signal characteristics were defined. The classification results were obtained when the SVM method was applied compared to the k-nearest neighbors method applying 1-nn in general. The tests were carried out within the database of patient voice recordings collected in the Department of Neurology at the Medical University of Warsaw. The second stage of the research was the selection of descriptors. The SFFS (sequential floating forward) method was applied together with the k-nn and SVM classifier. These subsets were used to create a new system based on a descriptor loose integration. Within the experiments conducted, general diagnosis results lead to improved classifier performance only in certain cases. This prompted the authors to conduct the last experimental research stage—selection at the feature fusion stage. Feature evaluation ranking methods (Relief, Fisher Score, F-tests, Chi-square) were applied for this purpose. With 10-fold validation, the k-nn method achieved an recognition rate of 92.2% with 91.1% sensitivity and 93.3% specificity.

Supervision of Instruction: Teachers and School Heads’ Perspective

The research focused on discovering the tactics used by school heads and the challenges faced when overseeing teaching in selected public secondary schools in Puerto Princesa City Division. The research utilized descriptive, correlation methods. The study included 139 participants, with 124 being teachers and 15 being school heads from the 15 public secondary schools in Puerto Princesa City Division. The researcher collected data from a survey questionnaire they created, the School Improvement Plan (SIP), The data was analyzed using frequency, percentage, mean, weighted mean, ranking, Pearson-r correlation, and t-test methods. The results showed that the school heads’ supervisory strategies focused on enhancing instruction. They informed the teachers about the professional development opportunities that are available to them in certain areas. They frequently conducted their instructional supervision. The most popular functions were giving teachers feedback through performance assessment and evaluation that was constructive and accurate. The mentoring abilities of the school heads, along with the passive and uncooperative behavior of teachers, hindered their ability to supervise instruction effectively. The school heads’ instructional supervisory strategies were influenced by the frequency of instructional supervision provided to teachers, as they focused on developing, adopting, and implementing programs for instructional improvement, learning assessment, and teacher improvement. The research suggests that during mentorship sessions, school administrators should provide teachers with advice, specifically focusing on teaching techniques and the discovery and use of educational materials; Within the school improvement initiative, it is critical for school heads to establish or modify a program based on research. School administrators need to collaborate with expert teachers to evaluate and improve the curriculum in order to address the unique requirements of the community.

Longitudinal Benefit:risk Analysis through the Desirability of Outcome Ranking (DOOR) with Application to ACTT-1 Trial

A clinical trial aims to uncover high-quality evidence for both efficacy and safety for an experimental treatment. Traditionally, statisticians evaluate the two aspects marginally, where, for example, efficacy considers the proportion of patients cured and safety considers the proportion of patients not showing adverse events. However, separate analyses could end up with misleading results, as the cumulative nature of clinical outcomes and the correlation between efficacy and safety endpoints are neglected. The desirability of outcome ranking (DOOR) method addresses such issues and provides a patient-centric approach to benefit:risk evaluation. A patient’s outcome is ranked based on pre-specified clinical criteria, where the most desirable rank represents a good outcome with no side effects and the least desirable rank is the worst possible clinical outcome. As the DOOR outcome is a temporal state that can have repeated measures, we propose a longitudinal approach that estimates and infers the temporal treatment effects. We develop a methodology for constructing simultaneous confidence bands by accounting for the correlations across time points. Additionally, we propose a weighted Mann-Whitney-U statistic to evaluate the treatment effect over the entire trial period. The performance of the proposed methodologies is examined through simulations and an application to a COVID-19 trial.

Dynamic localization based-utility decision approach under type-2 Pythagorean fuzzy set for developing internet of modular self-reconfiguration robot things

The Internet of Modular Robot Things (IoMRT) has emerged through the integration of robotic systems into the Internet of Things (IoT), offering a wide range of solutions to meet continuously growing demands. Six self-reconfiguration functionalities/criteria have been proposed for developing IoMRT. However, no study has fully developed an IoMRT that satisfies all the necessary functionalities. Additionally, there is a lack of scholarly research proposing a decision-based approach for evaluating and ranking IoMRT, which highlights a significant research gap. A complex multiple-criteria decision-making (MCDM) problem has arisen in evaluating and ranking IoMRT due to the diversity of functionalities, the need to prioritize these functionalities based on their importance, and data variability. To address this issue, the study proposes a novel decision-based approach for evaluating and ranking IoMRT, which consists of three phases: (i) Developing a novel weighting method called T2PFS-FWZICbIP (Type-2 Pythagorean Fuzzy Set–Fuzzy Weighted Zero Inconsistency based on Interrelationship Process) to measure the importance of the identified functionalities; (ii) Formulating a decision matrix by cross-referencing potential IoMRT developments with the six self-reconfiguration functionalities resulted in the selection of a random sample of 50 IoMRTs as proof of concept. Following this, the DLbU (Dynamic Localization-based Utility) method was proposed, integrating dynamic localization and utility procedures to manage binary data within the decision matrix; (iii) Developing a novel ranking method, T2PFS-DNMA (Type-2 Pythagorean Fuzzy Set–Double Normalization-based Multiple Aggregation), to address the diversity of functionalities and concerns regarding data variance. The results revealed that the Distributed functionality (C1) received the highest weight value of 0.4060 according to T2PFS-FWZICbIP, indicating its high importance in the ranking of IoMRT. In contrast, the High-Fidelity functionality (C5) received a weight value of 0.0733, indicating its very low importance in the ranking. IoMRT2 and IoMRT35 were identified as the most and least favored, respectively, according to T2PFS-DNMA. The robustness of the proposed approach was assessed through sensitivity analysis and comparative studies.

A VIKOR-Based Sequential Three-Way Classification Ranking Method

VIKOR uses the idea of overall utility maximization and individual regret minimization to afford a compromise result for multi-attribute decision-making problems with conflicting attributes. Many researchers have proposed corresponding improvements and expansions to make it more suitable for sorting optimization in their respective research fields. However, these improvements and extensions only rank the alternatives without classifying them. For this purpose, this text introduces the three-way sequential decisions method and combines it with the VIKOR method to design a three-way VIKOR method that can deal with both ranking and classification. By using the final negative ideal solution (NIS) and the final positive ideal solution (PIS) for all alternatives, the individual regret value and group utility value of each alternative were calculated. Different three-way VIKOR models were obtained by four different combinations of individual regret value and group utility value. In the ranking process, the characteristics of VIKOR method are introduced, and the subjective preference of decision makers is considered by using individual regret, group utility, and decision index values. In the classification process, the corresponding alternatives are divided into the corresponding decision domains by sequential three-way decisions, and the risk of direct acceptance or rejection is avoided by putting the uncertain alternatives into the boundary region to delay the decision. The alternative is divided into decision domains through sequential three-way decisions, sorted according to the collation rules in the same decision domain, and the final sorting results are obtained according to the collation rules in different decision domains. Finally, the effectiveness and correctness of the proposed method are verified by a project investment example, and the results are compared and evaluated. The experimental results show that the proposed method has a significant correlation with the results of other methods, ad is effective and feasible, and is simpler and more effective in dealing with some problems. Errors caused by misclassification is reduced by sequential three-way decisions.

Knowledge enhanced edge-driven graph neural ranking for biomedical information retrieval

Neural networks used for information retrieval tend to capture textual matching signals between a query and a document. However, neural ranking models for biomedical information retrieval often struggle to semantically well match the query to the documents. The main reasons are that biomedical terms have many different representations and the fact description related to the query is non-consecutive and non-local in the documents. In this paper, we present an edge-driven graph neural ranking method for biomedical information retrieval by incorporating knowledge from medical databases. First, we propose to form an edge-driven graph by connecting some biomedical terms in the query and the document through different types of edges. Then, we design a novel way of knowledge integration to introduce knowledge related to biomedical terms into the graph and construct a knowledge-query-doc graph. Finally, a graph neural ranking model is applied to capture non-local and non-contiguous match signals between the query and the document. Experimental results show on the biomedical datasets that our method outperforms the advanced neural models. And further analysis shows that the knowledge integration method can well reduce the semantic gap between the query and the document, and our graph model can provide interpretation for matching between the query and the document.

Analysis of Constraints Faced in the Bt Cotton Seed Market in Devbhumi Dwarka District

This study provides an in-depth analysis of the constraints affecting the Bt cotton seed market for farmers and dealers in Devbhumi Dwarka district, Gujarat. Although Bt cotton has revolutionized cotton production by increasing yield and reducing pesticide use, numerous challenges persist in seed acquisition and sales. This research investigates constraints related to seed availability, pricing, demand-supply gaps, and competition. Data were collected through structured interviews with 120 farmers and 20 dealers and analyzed using Garrett's ranking method to prioritize the constraints. Results indicate that limited availability of preferred seed brands, high prices, and untimely supply are the primary issues for farmers. Dealers face challenges, including inventory shortages due to demand-supply gaps, high competition, and credit sales pressures. This paper suggests strategies to streamline the seed distribution process and alleviate the constraints faced by stakeholders in the Bt cotton seed market.

Reduction of Vision-Based Models for Fall Detection.

Due to the limitations that falls have on humans, early detection of these becomes essential to avoid further damage. In many applications, various technologies are used to acquire accurate information from individuals such as wearable sensors, environmental sensors or cameras, but all of these require high computational resources in many cases, delaying the response of the entire system. The complexity of the models used to process the input data and detect these activities makes them almost impossible to complete on devices with limited resources, which are the ones that could offer an immediate response avoiding unnecessary communications between sensors and centralized computing centers. In this work, we chose to reduce the models to detect falls using images as input data. We proceeded to use image sequences as video frames, using data from two open source datasets, and we applied the Sparse Low Rank Method to reduce certain layers of the Convolutional Neural Networks that were the backbone of the models. Additionally, we chose to replace a convolutional block with Long Short Term Memory to consider the latest updates of these data sequences. The results showed that performance was maintained decently while significantly reducing the parameter size of the resulting models.

A novel ranking method in data envelopment analysis: a real case on Chinese banking industry

Purpose This study aims to address the lack of discrimination between fully efficient decision-making units in nonparametric efficiency analysis models by introducing a new ranking technique that incorporates contextual variables. Design/methodology/approach The proposed method combines Data Envelopment Analysis (DEA) and Ordinary Least Squares (OLS). First, DEA evaluates the partial efficiency of each unit, considering all inputs and only one output. Next, OLS removes the influence of contextual variables on the partial efficiencies. Finally, a ranking criterion based on modified partial efficiencies is formulated. The method is applied to data from 100 Chinese banks, including state-owned, commercial and industrial institutions, for the year 2020. Findings The ranking results show that the top six positions are assigned to highly esteemed banks in China, demonstrating strong alignment with real-world performance. The method provides a comprehensive ranking of all units, including nonextreme efficient ones, without excluding any. It resolves infeasibility issues that arise during the ranking of efficient units and ensures uniqueness in efficiency scores, leading to a more reliable and robust ranking process. Contextual variables exerted a greater influence on the first partial efficiency compared to the second. Notably, Total Capital Adequacy (TCA) significantly impact bank efficiency. Originality/value This study introduces a novel ranking method that effectively integrates contextual variables into DEA-based efficiency analysis, addressing limitations of existing methods. The practical application to Chinese banks demonstrates its utility and relevance.

Random survival forest algorithm for risk stratification and survival prediction in gastric neuroendocrine neoplasms.

This study aimed to construct and assess a machine-learning algorithm designed to forecast survival rates and risk stratification for patients with gastric neuroendocrine neoplasms (gNENs) after diagnosis. Data on patients with gNENs were extracted and randomly divided into training and validation sets using the Surveillance, Epidemiology, and End Results database. We developed a prediction model using 10 machine learning algorithms across 101 combinations to forecast cancer-related mortality in patients with gNENs, selecting the best model using the highest mean over a sequence of time-dependent area under the receiver operating characteristic (ROC) curve (AUC). The performance of the final model was assessed through time-dependent ROC curves for discrimination and calibration curves for calibration. The maximum selection rank method was used to determine the best prognostic risk score threshold for classifying patients into high- and low-risk groups. Afterward, Kaplan-Meier analysis and log-rank test were used to compare survival rates among these groups. Our study examined 775 patients with gNENs, dividing them into training and validation sets. A training set comprised 543 patients, with a median follow-up of 42 months and cumulative mortality rates of 40.0% at 1 year, 48.6% at 3 years, and 54.0% at 5 years. A validation set comprised 232 patients, with cumulative mortality rates of 29.1% at 1 year, 43.5% at 3 years, and 53.2% at 5 years. The optimal random survival forest (RSF) model (mtry = 4, node size = 5) achieved an AUC of 0.839 for survival prediction in the training set. Comprising 11 variables such as demographics, treatment details, tumor characteristics, T staging, N staging, and M staging, the RSF model revealed high predictive accuracy with AUCs of 0.92, 0.96, and 0.96 for 1-, 3-, and 5-year survival, respectively, which was consistently reflected in the validation set with AUCs of 0.88, 0.92, and 0.89, respectively. Moreover, patients were risk-stratified. Although our RSF model effectively stratified patients into different prognostic groups, it needs external validation to confirm its utility for noninvasive prognostic prediction and risk stratification in gNENs. Further research is required to verify its broader clinical applicability.