Ranking Method Research Articles

Working capacity level defines the specific impairment profile of the comprehensive ICF core set for multiple sclerosis

Multiple sclerosis (MS) unfavorably affects working capacity. The Comprehensive International Classification of Functioning, Disability and Health Core Set for MS (cICF-MS), issued by the World Health Organization, has not yet been extended to evaluate working capacity level (WCL). To evaluate the relative importance of cICF-MS categories in relation to WCL. Persons with MS (PwMS), N = 129, who had been referred to Lithuania’s Disability and Working Capacity Assessment Office for WCL determination, were divided into three groups according to the percentage of remaining WCL (WCL1 had 0–25%, WCL2 had 30–40%, and WCL3 had 45–55%). Data regarding the cICF-MS categories were collected through telephone interviews and patient documentation. Using the fractional ranking method, the mean values of cICF-MS impairment were ranked from the most severely affected to the least affected (rank 1–93). Ranks with the 10 highest mean values of impairment severity in each WCL group were included in a descriptive analysis. In the WCL1 and WCL2 groups, the most-affected cICF-MS categories reflected disability related to gait and motor function. The WCL3 group presented with pain, fatigue, and impairments to visual acuity, psychic stability, urination, and memory. This study has identified specific cICF-MS impairment profiles based on remaining WCL.

Using a Flexible Risk Priority Number Method to Reinforce Abilities of Imprecise Data Assessments of Risk Assessment Problems

Risk priority number (RPN) is the most commonly used failure risk ranking method among all risk assessment methods. However, the traditional RPN method not only cannot handle incomplete information and hesitation information (such as hesitation information between the s5=Low and s6=Moderate) provided by experts, but it also does not consider the objective weights of risk assessment factors. These reasons will lead to biased conclusions, causing decision makers to make wrong judgments. To address the limitations of the traditional RPN technique, the aim of this paper is to propose the flexible RPN assessment method under an uncertain environment. The flexible RPN assessment method is an extension of the traditional RPN technique. The flexible RPN method integrates the traditional RPN method and interval-valued 2-tuple weighted average method, and, simultaneously, considers subjective weights and objective weights. In the numerical verification, this study has adopted the example of stages of treatment planning for proton beam radiation therapy to verify the correctness and validity of the proposed flexible RPN technique. The numerical results show that the proposed flexible RPN technique not only handles the hesitation and incomplete information provided by experts but also considers the subjective and objective weights of risk assessment factors, providing more reasonable ranking results in the risk analysis.

Assessing blood flow in uterine fibroids using intravoxel incoherent motion imaging compared with dynamic contrast-enhanced MRI

To assess the utility of IVIM parameters in evaluating uterine fibroid blood flow compared to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived blood flow. Sixteen premenopausal women with uterine fibroids were enrolled in this prospective study. Pelvic MRI scans were obtained for each subject, both with and without continuous intravenous infusion of oxytocin, known to decrease significantly uterine fibroid blood flow, to assess the changes in blood flow of uterine fibroids. IVIM and DCE analyses were conducted using separate dedicated software. The bi-exponential IVIM model was used to estimate perfusion fraction (f), pseudo-diffusion coefficient (D*), and diffusion coefficient (D). DCE blood flow values were derived via T1 perfusion deconvolution arithmetic, utilizing a first pass of an AIF curve. The correlation between the parameters were analyzed by Spearman’s rank correlation analysis due to small sample size. Means of the parameters were compared with a nonparametric Wilcoxon sign rank method for each pair. Statistically significant positive correlations were found between perfusion fraction values and DCE blood flow values with oxytocin (Spearson’s ρ = 0.78, p = 0.0004), and between fD* values and DCE blood flow values with oxytocin (Spearson’s ρ = 0.64, p = 0.0071). Significant differences in blood flow were detected across most IVIM parameters: f, D*, and fD* (p < 0.001, p = 0.0027, and p = 0.0002, respectively) when comparing the values without oxytocin and with oxytocin. IVIM imaging shows promise for assessing blood flow in uterine fibroids.

Integrating Autonomous Shuttles: Insights, Challenges, and Strategic Solutions from Practitioners and Industry Experts’ Perceptions

Integrating autonomous shuttles into public transportation systems holds immense potential to revolutionize urban mobility and enhance accessibility. This paper focuses on a comprehensive analysis of the perceptions of practitioners and industry experts and proposes best practices for effectively integrating autonomous shuttles into public transportation systems. Perceptions of stakeholders have been collected, and a two-fold analysis was performed. Critical barriers for the adoption of autonomous shuttles were identified using the Garette ranking method and principal component analysis (PCA). Recommendations covering different aspects, including underutilization, safety concerns, seating arrangements, reliability, data security, operational aspects, sensor technology, and lane use, are provided. They encompass operational adjustments, infrastructure enhancements, safety measures, policy considerations, and economic foresight. The findings emphasize the importance of extending pilot deployment trial periods, improving autonomy, strategically positioning sensors, enhancing road signage, and providing dedicated lanes for autonomous shuttles. Data-security policies, operator training, and stakeholder responsibilities are also highlighted to build trust and facilitate a seamless transition to autonomous shuttles. This paper concludes by providing recommendations to ensure the successful integration of autonomous shuttles, fostering widespread acceptance and shaping the future of urban transportation.

Socio-economic backwardness assessment modelling in different gram panchayats of Sali watershed using multi-criteria based weighting and ranking methods

Socio-economic backwardness assessment modelling in different gram panchayats of Sali watershed using multi-criteria based weighting and ranking methods

Using Multilabel Neural Network to Score High‐Dimensional Assessments for Different Use Foci: An Example with College Major Preference Assessment

AbstractScoring high‐dimensional assessments (e.g., &gt; 15 traits) can be a challenging task. This paper introduces the multilabel neural network (MNN) as a scoring method for high‐dimensional assessments. Additionally, it demonstrates how MNN can score the same test responses to maximize different performance metrics, such as accuracy, recall, or precision, to suit users' varying needs. These two objectives are illustrated with an example of scoring the short version of the College Majors Preference assessment (Short CMPA) to match the results of whether the 50 college majors would be in one's top three, as determined by the Long CMPA. The results reveal that MNN significantly outperforms the simple‐sum ranking method (i.e., ranking the 50 majors' subscale scores) in targeting recall (.95 vs. .68) and precision (.53 vs. .38), while gaining an additional 3% in accuracy (.94 vs. .91). These findings suggest that, when executed properly, MNN can be a flexible and practical tool for scoring numerous traits and addressing various use foci.

دراسة مقارنة بين نظام الحمأة المنشطة ومرشحات التنقيط لمعرفة أي الأنظمة يتطلب مساحة أرض أقل

The rising cost of land across the globe has led to economic problems. One of the challenges brought by the high land price is constructing wastewater treatment systems with a small footprint. This paper compares the design of two domestic wastewater treatment systems, the activated sludge process (ASP) and trickling filter (TF), to determine which design requires the least area of land. The conventional activated sludge process design used the ASP equation, and the TF design used Rankin's method. The results showed that both systems have good removal efficiency. The ASP has a surface area of 3864 m2, and the surface area for the TF is 5000 m2. Therefore, the ASP is the better alternative when constructing a wastewater treatment system on a small land area. Keywords: Activated Sludge, Trickling Filter, Wastewater Treatment Plants, Biological Oxygen Demand.

Technologies of STEM+ education in educational organizations of secondary vocational education

Introduction. STEM education focused on the integration of scientific, technological, engineering and mathematical disciplines in the conditions of intensification of economic and social processes has become widespread in the economically developed countries of the world. In this regard, the analysis of conditions for quality training of qualified personnel for the innovation economy is in the center of attention as the basis for movement towards the development of technological sovereignty. The aim of the study is to analyze the features of STEM+ education technologies application in Russian educational organizations implementing secondary vocational education programs based on the data obtained during empirical research. Materials and methods. We conducted the study by questioning the heads and methodologists of organizations implementing secondary vocational education programs with electronic form of information collection. We processed the obtained data based on rating analysis by the method of systematization and ranking of results. The study involved 75 educational organizations from 46 constituent entities of the Russian Federation. Results. We have identified the features of implementation of STEM+ education technologies in professional educational organizations in the process of training qualified personnel in the areas of technological sovereignty projects. We have identified the main risks and barriers to the development of STEM+ education in the context of Russian realities. According to the survey data, these include the problems of education financing (over 53% of survey participants pointed to this problem), equipment with modern equipment and software, which indicated 54.6% of survey participants. We found insufficient level of teachers' readiness in the answers of more than 75% of the survey participants. There is a lack of professional reflection on the need to use STEM+ education technologies to intensify the educational process. The leading role in the introduction of STEM+ education technologies in the educational process by organizations implementing secondary vocational education programs belongs to the factors of training of teaching staff to use modern technologies in the educational process, as evidenced by up to 80% of the responses of the study participants. Prospects for further research. The obtained results can be useful in studying the conditions and prospects of STEM+ education development for the training of qualified personnel in professional educational organizations.

The Runge-Kutta Numerical Method of Rank Seven for the Solutions of Some Refined Neutrosophic Differential Problems

In This paper, we present a numerical approach to the seventh rank refined neutrosophic Runge-Kutta numerical method, where we provide the theoretical basis of this formula to be applicable on refined neutrosophic differential equations. In addition, we provide numerical tables to compare the validity of this new method with other methods, as well as a clear computation of absolute errors in terms of refined neutrosophic numbers.

A Commensurate Univariate Variable Ranking Method for Classification

A Commensurate Univariate Variable Ranking Method for Classification

Enhance the Understanding of Learning Sorting and Searching Algorithms through Card Ranking Methods (Game-Based Approach)

Enhance the Understanding of Learning Sorting and Searching Algorithms through Card Ranking Methods (Game-Based Approach)

Application of MRI-based tumor heterogeneity analysis for identification and pathologic staging of breast phyllodes tumors.

To explore the application value of MRI-based imaging histology and deep learning model in the identification and classification of breast phyllodes tumors. Seventy-seven patients diagnosed as breast phyllodes tumors and fibroadenomas by pathological examination were retrospectively analyzed, and traditional radiomics features, subregion radiomics features, and deep learning features were extracted from MRI images, respectively. The features were screened and modeled using variance selection method, statistical test, random forest importance ranking method, Spearman correlation analysis, least absolute shrinkage and selection operator (LASSO). The efficacy of each model was assessed using the subject operating characteristic (ROC) curve, The DeLong test was used to assess the differences in the AUC values of the different models, and the clinical benefit of each model was assessed using the decision curve (DCA), and the predictive accuracy of the model was assessed using the calibration curve (CCA). Among the constructed models for classification of breast phyllodes tumors, the fusion model (AUC: 0.97) had the best diagnostic efficacy and highest clinical benefit. The traditional radiomics model (AUC: 0.81) had better diagnostic efficacy compared with subregion radiomics model (AUC: 0.70). De-Long test, there is a statistical difference between the fusion model traditional radiomics model, and subregion radiomics model in the training group. Among the models constructed to distinguish phyllodes tumors from fibroadenomas in the breast, the TDT_CIDL model (AUC: 0.974) had the best predictive efficacy and the highest clinical benefit. De-Long test, the TDT_CI combination model was statistically different from the remaining five models in the training group. Traditional radiomics models, subregion radiomics models and deep learning models based on MRI sequences can help to differentiate benign from junctional phyllodes tumors, phyllodes tumors from fibroadenomas, and provide personalized treatment for patients.

PHYSICS BASED ALGORITHMS FOR FUZZY TRANSPORTATION PROBLEM

Transportation problems are computationally challenging due to their nondeterministic polynomial-time hard (NP-hard) nature, especially when uncertainties in costs, supply, and demand are involved. The fuzzy transportation problem addresses these uncertainties by representing them as fuzzy numbers, requiring robust methods for ranking and solving optimization models. Traditional exact methods become impractical for such problems, leading to the adoption of metaheuristic approaches. This study introduces three novel physics-based algorithms: one single-point-based and two population-based methods. These algorithms leverage principles from gravitational attraction, electromagnetism, and water flow dynamics, incorporating innovative neighborhood structures tailored to the fuzzy nature of transportation problems. The proposed methods were tested on diverse problem sizes and compared against established optimization techniques, such as genetic algorithms and commercial software, using Python implementations. An experimental design methodology was employed to optimize parameter tuning, enhancing computational efficiency and reducing experimentation time. Results demonstrate that the physics-based algorithms achieve competitive performance in terms of solution quality, computational efficiency, and adaptability to uncertainty. These findings underscore the potential of physics-based methods to advance optimization in fuzzy transportation and other domains with inherent uncertainties, paving the way for further research in hybrid and multi-objective approaches.

RankSNN: A Ranking Method to Integrated Evaluation

RankSNN: A Ranking Method to Integrated Evaluation

Predicting Heart Rate at the Anaerobic Threshold Using a Machine Learning Model Based on a Large-Scale Population Dataset.

Background/Objectives: For effective exercise prescription for patients with cardiovascular disease, it is important to determine the target heart rate at the level of the anaerobic threshold (AT-HR). The AT-HR is mainly determined by cardiopulmonary exercise testing (CPET). The aim of this study is to develop a machine learning (ML) model to predict the AT-HR solely from non-exercise clinical features. Methods: From consecutive 21,482 cases of CPET between 2 February 2008 and 1 December 2021, an appropriate subset was selected to train our ML model. Data consisted of 78 features, including age, sex, anthropometry, clinical diagnosis, cardiovascular risk factors, vital signs, blood tests, and echocardiography. We predicted the AT-HR using a ML method called gradient boosting, along with a rank of each feature in terms of its contribution to AT-HR prediction. The accuracy was evaluated by comparing the predicted AT-HR with the target HRs from guideline-recommended equations in terms of the mean absolute error (MAE). Results: A total of 8228 participants included healthy individuals and patients with cardiovascular disease and were 62 ± 15 years in mean age (69% male). The MAE of the AT-HR by the ML-based model was 7.7 ± 0.2 bpm, which was significantly smaller than those of the guideline-recommended equations; the results using Karvonen formulas with the coefficients 0.7 and 0.4 were 34.5 ± 0.3 bpm and 11.9 ± 0.2 bpm, respectively, and the results using simpler formulas, rest HR + 10 and +20 bpm, were 15.9 ± 0.3 and 9.7 ± 0.2 bpm, respectively. The feature ranking method revealed that the features that make a significant contribution to AT-HR prediction include the resting heart rate, age, N-terminal pro-brain natriuretic peptide (NT-proBNP), resting systolic blood pressure, highly sensitive C-reactive protein (hsCRP), cardiovascular disease diagnosis, and β-blockers, in that order. Prediction accuracy with the top 10 to 20 features was comparable to that with all features. Conclusions: An accurate prediction model of the AT-HR from non-exercise clinical features was proposed. We expect that it will facilitate performing cardiac rehabilitation. The feature selection technique newly unveiled some major determinants of AT-HR, such as NT-proBNP and hsCRP.

Machine Learning-Based Reconnaissance Bee Colony with Custom AES &amp; AE for Robust IoT Data Security and Transmission

Objectives: To propose a robust method to enhance data security and transmission in IoT to address the challenges in real-time data transmission and create a comprehensive security solution. The new method takes credit for the machine learning-based bioinspired method to minimize data loss and maximize end-to-end data transmission rate. Methods: Custom-AES with Avalanche Effect (AE) and Machine Learning-based Reconnaissance Bee Colony are employed to secure the data with enhanced encryption by creating a multi-layered IoT security solution. Key stretching is used to provide high resistance to cryptographic attacks. In order to optimize threat detection, Pareto-based selection and dynamic parameter tuning with ML-RBC are used. To improve attack resilience, rank, and tournament selection methods are employed to target DDoS and MMIT attacks with a minimized encryption time. The NSL-KDD IoT network dataset is used to test the proposed CAES-AE with the ML-RBC model using a network simulator. To assess the performance of the proposed work, the results are compared with prevailing IoT data transmission techniques such as ERSA, QoS-BFT, AES, Custom AES, and RBC. Findings: The suggested CAES-AE with ML-RBC data security and transmission method attains the promising results of 97% authentication success rate, 6 seconds response time to security actions, 96% vulnerability detection speed, 97% data encryption strength, 5 seconds incident response time, and 97.8% network performance (energy efficiency &amp; lifetime), which is higher than earlier methods. Novelty: This research provides a secured data transmission method in an IoT environment to address the real-time data transmission challenges faced in sensor-based networks. This will function as a multi-layered IoT security model that optimizes real-time attack detection and resilience. The performance metrics are evaluated with multiple packet transfer rates to boost the network performance. Keywords: Machine Learning, Advanced Networking, Data Security, Secured Data Transmission, Bio­Inspired Algorithm

A decision support system for comments-adjusted ranking of hotels

Increasing online reviews provide a powerful support tool to obtain a relevant hotel selection for tourists. Some reviews seem to have similar contents but their latent meanings are discrepant, due to user preferences. It has a negative effect on the recommendation result without a well processing. To solve this issue, this study develops a cloud-based ranking method to provide hotel recommendation lists for tourists. The method uses the distribution of historical user ratings to adjust original user ratings for minimizing the understanding bias among reviews, and utilizes sentiment analysis and cloud models to quantify text comments. After these, both of them are merged into a holistic score to generate a hotel recommendation list. Our method is validated through a case study of 87,735 users and 835,462 reviews from TripAdvisor.com. Further, our research findings suggest that potential hotel stayers should realize to lower their expectations of a good stay from online reviews, and trust more online reviews of couple and friend travelers.

Influence of organic fertilization on the economic efficiency of field lettuce production

In our country lettuce is widely cultivated as a field and greenhouse crop. A significant part of the production during the autumn-winter and winter-spring period takes place in unheated polyethylene greenhouses. An important factor in the choice of variety is, on the one hand, the quality indicators in line with market requirements, which have been the subject of a number of studies at home and abroad, and, on the other, the possibility of achieving high profitability of production. The aim of the study is to evaluate the economic effects of the application of organic fertilizers in the production of field lettuce. The comparative economic evaluation of different fertilizer options is carried out using the ranking method. The most attractive fertilizers for practice are Italpolina and Lumbicompost.

Dynamic Evaluation of Adaptive Product Design Concepts Using m-Polar Linguistic Z-Numbers

Adaptive design focuses on creating flexible products that meet evolving demands and enhance sustainability. However, evaluating adaptive design concepts poses significant challenges due to the dynamic nature of product features over time and the inherent uncertainty in decision-makers’ (DMs’) evaluations. Most traditional frameworks rely on static models that fail to capture the temporal evolution of attributes and often overlook decision-makers’ (DMs’) confidence levels, resulting in incomplete or unreliable evaluations. To bridge these gaps, we propose the m-polar linguistic Z-number (mLZN) to address these issues. This framework uses the dynamic representation capabilities of m-polar fuzzy sets (mFSs) and the symmetrical structure of linguistic Z-numbers (LZNs), which effectively integrate linguistic evaluations with corresponding confidence levels, providing a balanced and robust approach to handling uncertainty. This approach models design characteristics across multiple periods while accounting for DMs’ confidence levels. Based on this framework, we develop mLZN weighted and geometric aggregation operators, computation rules, and ranking methods to support dynamic multi-attribute group decision-making (MAGDM). The proposed framework’s effectiveness is demonstrated through a case study on adaptive furniture design for children, which showcases its ability to dynamically evaluate key attributes, including safety, ease of use, fun, and comfort. Furthermore, we validate its robustness and feasibility through comprehensive sensitivity and comparative analyses.

Diagnostic value of high-frequency ultrasound combined with magnetic resonance in different clinical stages of frozen shoulder: A retrospective cohort study

Background Frozen shoulder (FS) is a common musculoskeletal disease. Imaging plays an active role in the clinical diagnosis and staging of FS. Objective This study is undertaken to assess the application value of high-frequency ultrasound (HFU) combined with magnetic resonance (MR) in clinical staging diagnosis of FS. Methods This was a retrospective cohort study. The medical records of FS patients (from January 2021 to February 2022, n = 100) were collected. Patients were categorized into stage I (n = 30), stage II (n = 35) and stage III (n = 35) groups. The diagnostic value of HFU-related and MR-related indicators was assessed by the receiver operating characteristic (ROC) curve. The correlation between HFU/MR indicator and passive range of motion-related indicators was analyzed by Spearman rank method. Results There were no statistically significant differences in gender, age, height, weight, and affected shoulder (left: right) among the three groups at different stages ( p &gt; 0.05). HFU and MR in combination had a higher diagnostic coincidence rate when compared to their single use (96.00% vs. 91.00% or 93.00%). There was high diagnostic value for inferior capsule thickness (ICT) (area under the curve (AUC) = 0.914, p &lt; 0.001, 95% CI (0.836–0.992)), glenohumeral distance (GHD) (AUC = 0.999, p &lt; 0.001, 95% CI (0.996–1.000)) and articular capsule thickness (ACT) (AUC = 1.000, p &lt; 0.001, 95% CI (0.998–1.000)) in stage I FS, for ICT (AUC = 0.947, p &lt; 0.001, 95% CI (0.885–1.000)) and GHD (AUC = 0.974, p &lt; 0.001, 95% CI (0.949–0.999)) in stage II FS, and for ACT (AUC = 0.989, p &lt; 0.001, 95% CI (0.975–1.000)) in stage III FS. ICT/ACT positively correlated with internal rotation (IR), and negatively correlated with external rotation (ER)/extension (EX)/flexion (FL)/abduction (AB). GHD had a negative correlation with IR, and a positive correlation with ER/EX/FL/AB. Conclusion High-frequency ultrasound combined with magnetic resonance can improve the accuracy in clinical diagnosis of FS, promote better staging of FS, and provide a more reliable basis for clinical diagnosis and treatment of FS.