Performance Ranking Research Articles

Examining different cost ratio frameworks for decision rule machine learning algorithms in diagnostic application.

Artificial Intelligence (AI) plays a pivotal role in the diagnosis of health conditions ranging from general well-being to critical health issues. In the realm of health diagnostics, an often overlooked but critical aspect is the consideration of cost-sensitive learning, a facet that this study prioritizes over the non-invasive nature of the diagnostic process whereas the other standard metrics such as accuracy and sensitivity reflect weakness in error profile. This research aims to investigate the total cost of misclassification (Total Cost) by decision rule Machine Learning (ML) algorithms implemented in Java platforms such as DecisionTable, JRip, OneR, and PART. An augmented dataset with conjunctiva images along candidates' demographic and anthropometric features under supervised learning is considered with a specific emphasis on cost-sensitive classification. The opted decision rule classifiers use the text features, additionally the image feature 'a* value of CIELAB color space' extracted from the conjunctiva digital images as input attributes. The pre-processing consists of amalgamating text and image features on a uniform scale, normalizing. Then the 10-fold cross-validation enables the classification of samples into two categories: the presence or absence of the anemia. This study utilizes the Cost Ratio (ρ) extracted from the cost matrix to meticulously monitor the Total Cost in four different cost ratio methodologies namely Uniform (U), Uniform Inverted (UI), Non-Uniform (NU), and Non-Uniform Inverted (NUI). It has been established that the PART classifier stands out as the top performer in this binary classification task, yielding the lowest mean total cost of 629.9 compared to other selected classifiers. Moreover, it demonstrates a comparatively lower standard deviation 335.9, and lower total cost across all four different cost ratio methodologies. The ranking of algorithm performance goes as follows: PART, JRIP, DecisionTable, and OneR. The significance of adopting a cost-sensitive learning approach is emphasized showing the PART classifier's consistent performance within the proposed framework for learning the anemia dataset. This emphasis on cost-sensitive learning not only enhances the recommendations in diagnosis but also holds the potential for substantial cost savings and makes it a noteworthy focal point in the advancement of AI-driven health care.

A Hybrid Model for Fitness Influencer Competency Evaluation Framework

Fitness influencers are an emerging profession in recent years. At present, the main research on fitness influencers focuses on their personal traits, professional knowledge and skills, and course content, while there is still a large research gap on the social media marketing strategies of fitness influencers, how they interact with fans, and the reasons for their influence on fans. There is a lack of a comprehensive evaluation framework for fitness influencer research, and there is no clear research on what competencies are required to become a qualified fitness influencer. Therefore, it has become an important issue to establish a comprehensive fitness influencer competency evaluation. In this study, a hybrid model of fitness influencer competency evaluation framework was developed based on government competency standards and expert knowledge using the Multiple Criteria Decision-Making (MCDM) model perspective. This evaluation should expand to include the principles of sustainable development, emphasizing the influencers’ role in advocating for environmental responsibility, social equity, and economic viability within the fitness industry. First, the study developed 21 criteria in six dimensions of fitness influencer competencies through a literature survey and interviews with several experts. The 21 criteria resonate with many of the Sustainable Development Goals (SDGs), including SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), SDG 5 (Gender Equality), SDG 10 (Reduced Inequalities), and SDG 11 (Sustainable Cities and Communities). The Bayesian Best-Worst Method (Bayesian BWM) was used to generate the best group weights for fitness influencer competencies. Then, a modified Technique for Order Preference by Similarity to the Ideal Solution Based on Aspiration Level (modified TOPSIS-AL) was applied to evaluate the performance ranking of major fitness influencers in Taiwan by integrating the concept of the aspiration level. The results of the study revealed that behavioral standards were the most important dimension, emphasizing the need for fitness influencers to establish a comprehensive set of norms for their own behavioral standards. The top five criteria for fitness influencers’ competencies were self-review, punctuality and prudence, creativity, rapport and motivation, and the need to conform to one’s body image. The performance ranking was used to compare the evaluated subjects to the desired level to obtain a basis for improvement. This study effectively identifies key fitness industry competency indicators and refines business performance through the management implications proposed in this study to facilitate the development of the fitness industry.

A novel landslide susceptibility prediction framework based on contrastive loss

ABSTRACT Recently, the positive unlabeled (PU) learning algorithms have proven highly effective in generating accurate landslide susceptibility maps. The algorithms categorize samples exclusively into positive samples (landslides) and unlabeled samples for training, eliminating random or subjective selection of non-landslide samples. However, existing PU learning algorithms face limitations in capturing correct negative samples in multi-genesis landslide areas, leading to lower prediction accuracy. To address this issue, the PU-pullbaggingDT algorithm was proposed in this study. This integrated method combines the strengths of two techniques: the superior ranking performance of the PU-baggingDT algorithm and the low bias of the contrastive learning approach. The contrastive learning introduces a new contrastive loss of PU learning (PUpull loss), which pulls the distance of similar landslide samples in the projection space closer based on the validation accuracy threshold, without the need for data augmentation and class probability. The PUpull loss relaxes the tightness toward unlabeled samples, reducing the impact of incorrectly defined non-landslide samples on prediction results in multi-genesis landslide areas. The proposed algorithm outperforms existing PU-learning and machine learning methods (support vector machine, decision tree, logistic regression, AdaBoost, and XGBoost) with random selection of negative samples for predicting landslide susceptibility in China’s Zigui County, as demonstrated by comprehensive evaluation metrics. The landslide susceptibility mapping utilizes equal interval division and ranking, achieving approximately a 90% landslide percentage in areas with very high and high susceptibility in Zigui County. This demonstrates the capability of the proposed algorithm to effectively predict landslide susceptibility in complex geological settings.

Analyzing Profitability Performance with the Integrated MEREC-COBRA Method: The Case of BIST Retail Companies

The purpose of this study is to evaluate the financial performance of retail trade enterprises whose shares are traded on Borsa Istanbul by using profitability ratios. Within the scope of the study, the data of ten retail trade enterprises covering the period 2021-2022 were used. The weight of the performance criteria used in the study was determined by the MEREC method. COBRA method was used to determine the performance ranking among the firms. A two-stage sensitivity analysis was conducted to determine the consistency of the analysis results. In the first stage, seven different multi-criteria decision-making methods were used to rank the firms according to their performance. The consistency between the results of the study and the results of other methods was determined by Spearman correlation coefficients. In the second stage, scenarios with different criteria weights were tested. Sensitivity analysis showed that the results were stable and consistent. According to the results of the study, MIPAZ ranked first in profitability performance for both years.

Construction 4.0 technology evaluation using fuzzy TOPSIS: comparison between sustainability and resiliency, well-being, productivity, safety, and integrity.

This study aims to compare the impact of Construction 4.0 technologies on different organizational core values, focusing on sustainability and resiliency, well-being, productivity, safety, and integrity. To achieve that aim, the study objectives are the following: (i) identify the critical Construction 4.0 technologies between core values; (ii) appraise overlapping critical Construction 4.0 technologies between core values; (iii) examine the ranking performance of Construction 4.0 technologies between core values; and (iv) analyze the interrelationships between Construction 4.0 technologies and core values. First, twelve Construction 4.0 technologies were identified from a national strategic plan. Then, the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) that incorporates subjective and objective weights was used to evaluate the impact of the Construction 4.0 technologies on the five core values. Finally, the collected data was analyzed using the following techniques: fuzzy TOPSIS, normalization, overlap analysis, agreement analysis, sensitivity analysis, ranking comparison, and Spearman correlation. The study findings reveal four critical Construction 4.0 technologies that enhance all five core values: building information modeling (BIM), Internet of Things (IoT), big data and predictive analytics, and autonomous construction. Also, there is a high agreement on the Construction 4.0 technologies that enhance well-being and productivity. Lastly, artificial intelligence (AI) has the highest number of very strong relationships among the core values. The originality of this paper lies in its comprehensive comparison of the impact of Construction 4.0 technologies on multiple organizational core values. The study findings provide valuable insights in making strategic decisions in adopting Construction 4.0 technologies.

Keyword Pool Generation for Web Text Collecting: A Framework Integrating Sample and Semantic Information

Keyword pools are used as search queries to collect web texts, largely determining the size and coverage of the samples and provide a data base for subsequent text mining. However, how to generate a refined keyword pool with high similarity and some expandability is a challenge. Currently, keyword pools for search queries aimed at collecting web texts either lack an objective generation method and evaluation system, or have a low utilization rate of sample semantic information. Therefore, this paper proposed a keyword generation framework that integrates sample and semantic information to construct a complete and objective keyword pool generation and evaluation system. The framework includes a data phase and a modeling phase, and its core is in the modeling phase, where both feature ranking and model performance are considered. A regression model about a topic vector and word vectors is constructed for the first time based on word embedding, and keyword pools are generated from the perspective of model performance. In addition, two keyword generation methods, Recursive Feature Introduction (RFI) and Recursive Feature Introduction and Elimination (RFIE), are also proposed in this paper. Different feature ranking algorithms, keyword generation methods and regression models are compared in the experiments. The results show that: (1) When using RFI to generate keywords, the regression model using ranked features has better prediction performance than the baseline model, and the number of generated keywords is refiner, and the prediction performance of the regression model using tree-based ranked features is significantly better than that of the one using SHAP-based ranked features. (2) The prediction performance of the regression model using RFI with tree-based ranked features is significantly better than that using Recursive Feature Elimination (RFE) with tree-based one. (3) All four regression models using RFI/RFE with SHAP- based/tree-based ranked features have significantly higher average similarity scores and cumulative advantages than the baseline model (the model using RFI with unranked features). (4) Light Gradient Boosting Machine (LGBM) using RFI with SHAP-based ranked features has significantly better prediction performance, higher average similarity scores, and cumulative advantages. In conclusion, our framework can generate a keyword pool that is more similar to the topic, and more refined and expandable, which provides certain research ideas for expanding the research sample size while ensuring the coverage of topics in web text collecting.

Pemeringkatan Kinerja Dosen pada Perguruan Tinggi Swasta Menggunakan Algoritma Simple Additive Weighting

The Tri Dharma of Higher Education is an obligation for lecturers while carrying out their duties as lecturers at higher education institutions, the implementation of which is regulated in Law Number 20 of 2003 concerning the National Education System. The Tri Dharma of Higher Education is a lecturer's obligation, including Education and Teaching, Research and Development, and Community Service. Lecturers need Support and motivation to implement quality Tri Dharma, especially at "X" Private Universities. Providing rewards or awards can motivate lecturers to give their best performance to Tri Dharma. Student feedback is also needed as evaluation material for lecturers to measure their teaching abilities. Rewarding lecturers can be done by ranking lecturer performance, especially at private universities. The SAW (Simple Additive Weighting) algorithm ranks lecturer performance through the criteria of education and teaching, research, community service, and student feedback. An assessment of several subcriteria presents each criterion. The normalized scoring matrix is ​​the ranking preference. From the results of data processing on lecturer performance and feedback from students, with a sample of 25 lecturers, a ranking score was obtained on a scale of 0 to 1, where a score of 1 is the highest ranking. The lecturer performance ranking process involves lecturers, students, and the Study Program Management Unit. A lecturer performance rating information system is needed to facilitate all actors' involvement in the lecturer performance rating process and provide valid and timely rating results to stakeholders.

Multicriteria decision analysis tool for faculty performance evaluation in higher education institutions

Higher education institutions (HEIs), which are the major economic drivers in a nation, rely on their faculty for quality. The quality of faculty performance is assessed through performance appraisal methods. Performance appraisal methods help management assess the performance of employees and further the scope of improvement. Traditional methods of performance evaluation based on quantitative data involve more mathematical calculations than scientific approaches. The drawback of these methods is that they consider overall scores and not the individual factors of performance. This is exceptionally relevant when there are multiple disciplines in HEIs. In this study, the Multi-Criteria Decision Analysis (MCDA) model using MACBETH is used to explore its usefulness in explicitly dealing with the trade-offs between performance measures, making the process of performance measurement and management more transparent and defendable. Faculty performance across different disciplines was considered in this study, and faculty performance was evaluated using the MACBETH score. The advantage of MACBETH is that weight can be assigned to specific criteria, and accordingly, analysis can be carried out. Based on which criteria are assigned the highest weight, that factor is considered for the final output. In this study, the results obtained showed significant differences in the performance rankings of the faculty members when compared to those of the traditional methods, suggesting that the FSTs are useful tools for performing performance appraisals.

Does systemic risk affect fund managers' tail risk-taking?

This paper investigates the impact of systemic risk on the tail risk-taking behavior of fund managers, based on the data from China's mutual funds from 2011 to 2021. We construct a systemic risk indicator by utilizing industry indices and market macro state variables, and find that systemic risk can markedly inhibit the tail risk-taking behavior of fund managers. This inhibition effect is strengthened by the performance ranking of funds. Within the tournament effect framework, fund managers tend to be more risk averse in order to secure the realized return, as the fund's ranking is primarily motivated by compensation incentives rather than career concerns. Moreover, given that the portfolio associated with a fund forms a network, we find that the information mechanism can attenuate the inhibition effect that systemic risk indicators have on the tail risk-taking behavior of fund managers.

Local scour depth at piles group exposed to regular waves: On the assessment of expressions based on classification concepts and evolutionary algorithms

An accurate estimation of local scour depth around piles group is inevitably essential to provide stability of marine structures. Over the past decades, many investigations have been made to understand the scouring process at piles group exposed to waves for field and experimental scales. This study aims to predict the wave-induced local scour depth by various robust Data Driven Models (DDMs) and Machine Learning Models (MLMs) developed by classification and evolutionary concepts: Model Tree (MT), Evolutionary Polynomial Regression (EPR), Multivariate Adaptive Regression Spline (MARS), and Gene-Expression Programming (GEP). From relevant literature, 125 data series were employed to provide empirical relationships for scour depth predictions. The raw variables were related to bed sediment, pile configuration, pile geometry, approaching flow, and wave characteristics. Non-dimensional parameters have been obtained through the Buckingham theorem in order to control local scour depth. In this way, the ratio of spacing between piles to pile diameter (G/D), sediment number (Ns), pile arrangement number (ratio of the number of piles parallel to the flow; m; to the number of piles normal to the flow; n), Shields parameter (θ), and Keulegan-Carpenter (KC) were considered. From the training and testing stages of Artificial Intelligence Models (AIMs), it was found that regression equation given by MARS model provided better performance (e.g., Correlation Coefficient [R] = 0.9297, Root Mean Square Error [RMSE] = 0.3489, and Scatter Index [SI] = 0.2765) than other AI models’ efficiency. Additionally, the performance of AI models was assessed for various ranges of dimensionless parameters (i.e., G/D, Ns, and θ) versus KC variation. MARS model had the best performance for G/D = 0–1 and KC < 10 (R = 0.9917 and RMSE = 0.2198) and 0.4≤θ < 0.5 and 15 < KC < 25 whereas EPR model had promising efficiency in the its highest level for Ns = 1–3 and 10 < KC ≤ 15 (R = 0.9941 and RMSE = 0.0771) than other AI models. For the sensitivity analysis, the mutation rate and number of chromosomes are examined for the GEP model, the K-fold number and number of parameters for the MARS model, and the number of algebraic terms and number of chromosomes for the EPR model. Furthermore, ranking analysis of AI models indicated that MARS model had the best performance (Ranking Performance Index [RPI] = 0.7348) and followed by GEP (0.3412), M5MT (0.2999), and EPR (0.2848).

Penentu Industri Kecil Unggulan Dikabupaten Muaro Jambi Menggunakan Metode Simple Additive Weghting (SAW)

Small and Medium Industries (IKM) have an important role in economic development. Muaro Jambi Regency is one of the districts in Jambi Province whose people are engaged in small and medium industrial businesses (IKM). This is inseparable from the important role of the Muaro Jambi Regency Government in supporting the development of small and medium industries (IKM). SMEs in Muaro Jambi Regency are grouped into 5 groups, namely craft SMEs, chemical and building materials SMEs, metal and electronics SMEs, food SMEs, and clothing SMEs. Of these 5 groups, it is necessary to know which group of IKM is growing widely both in terms of the number of IKM, number of workers, investment value and production value. The aim of this research is to find out and analyze which small and medium industrial groups (IKM) are superior in Muaro Jambi district and help simplify the process of selecting superior SMEs in Muaro Jambi district. The analytical model used to determine priorities for leading small and medium industries in Muaro Jambi district is the SAW (Simple Additive Weighting) model. The SAW model is used to formulate a weighted sum of priorities for the performance ranking of each alternative on all attributes, while the system design method uses the System Prototyping method. The result of this research is the SPK system with the SAW model which shows that the leading small and medium industries in Muaro Jambi district selected based on predetermined criteria are the Chemical and Building Materials industry with a total of 0.813

Determining the financial performance of the firms in the Borsa Istanbul sustainability index: integrating multi criteria decision making methods with simulation

Regardless of the industry in which a company operates, evaluating corporate performance is one of the most critical and vital processes; the most essential and prominent performance evaluation is related to financial performance. Appropriate performance analysis is complex and critical for decision-makers in different financial performance factors; thus, a methodological framework is needed to solve such complex decision problems. Therefore, this research aims to rank the companies included in the sustainability index (excluding banks) in Turkey by considering their financial performance. The criteria weights were determined using the full consistency method (FUCOM) by considering the evaluations of four experts. The firms were ranked using nine multi-criteria decision-making methods. The consensus among the nine rankings was ensured with the Copeland technique. The decision matrix includes financial ratios and the stock market performance of the firms; 100,000 FUCOM weights were created with random evaluations to validate the results. The results indicate that the most crucial criterion is the current ratio by considering expert evaluations. Weight simulation indicates that alternative 16 (alternative 21) is superior (inferior) to the other alternatives, even though the weights are determined with random evaluations. Ranking with expert evaluations is similar to the mean of the weight simulation results. The results demonstrate that the proposed framework can be performed as a basis for financial performance ranking.

Increasing the Sensitivity of a Throne Method to Carbohydrate Using the MOORA Method

What are carbohydrates accurately? Glucose, or carbohydrates, are composed of sugar molecules. Together with amino acids and lipids, sweets are one of the 3 major nutrients found in meals and beverages. Your body breaks down carbs to produce glucose. Glucose, also referred to as glucose levels, is the main food supply for our body's tissues, tissues, and glands. As our main supplier of carbohydrates, processed carbs are crucial to a balanced diet. The Eatwell Guide indicates that starchy foods, such as tubers, bread, rice, pastas, and cereals, must make up little over a third of your diet. Glucose serves as the body's main fuel source. Throughout digestion, the digestion of sugars and carbs produces simple sugars. They are processed after entering the bloodstream, where they are recognized as serum sugar (blood glucose). The body then uses insulin to help glycogen access the cells. After the body carbohydrates in every cell and uses them for energy, as well as for interaction and physical stability on the surface of the cellular and as fuel in the plasma. Our blood and cells include sugars like glucose, which are used for rapid energy during cellular breathing. Alternative: Internal standard, Reducing monosaccharide, Authentic. Evaluation Preference: Fuc, Xyl, Man, Gal, Glc, GlcNAc, GalNAc, NeuNAc, GlcA, GalA. As a result, performance and first rank have been Glc. Whereas GalNAc is ranked low. MOORA method for carbohydrate the value of the dataset (based on ratio analysis multi-objective optimization) Glc shows that results in rankings.

Collaborative resource allocation-based differential evolution for solving numerical optimization problems

Differential evolution (DE) is an efficient and powerful population-based search algorithm for solving numerical optimization problems in continuous spaces. It has been proven that multi-strategy DE algorithms are more effective than single-strategy DE algorithms in addressing benchmark and real-world problems. However, most multi-strategy DE variants focus on maintaining population diversity and balancing exploitation and exploration, ignoring the dynamic allocation of computational resources. Moreover, the success of these algorithms often depends on additional designed techniques, leading to increased computational complexity. In this paper, the Collaborative Resource Allocation-based Differential Evolution (CRADE) is introduced. It involves a collaborative resource allocation mechanism that utilizes the historical performance ranking of three mutation strategies to automatically allocate computational resources to various subpopulations during the search process. The parameter adaptation technique is used to adjust the associated control parameters of different mutation strategies. As a result, the most efficient mutation strategy consumes the majority of computational resources at different search stages to mitigate inefficient search under constrained resources. The performance of CRADE is evaluated on the well-known CEC2013 benchmark function set. The paper also investigates its application in the parameter identification of photovoltaic solar cells and modules. The overall results show that CRADE exhibits superior and competitive performance compared to other state-of-the-art algorithms. Consequently, CRADE has emerged as a novel and effective approach for addressing numerical optimization problems, distinguished by its excellence, practicality, and unwavering reliability.

Key performance indicators-based monitoring, measuring progress and ranking of Aspirational Districts

The Aspirational District Program (ADP) is an effort to transform those districts which are lacking in development. It focuses on each district's strengths, identifies areas for quick development, and tracks advancement by rating districts regularly. By competing with and learning from others in the spirit of competitive &amp; cooperative federalism, districts are pushed and encouraged to first catch up with the best district within their state, and then aim to become one of the best in the nation. The government's "Sabka Saath Sabka Vikas aur Sabka Vishwas" initiative aims to improve residents' quality of life while promoting inclusive growth. The ADP fundamentally aims to localise the Sustainable Development Goals, resulting in national advancement. In this paper we have described the delta rankings, Key performance Indicators (KPI) and the effects on development of aspirational districts. The delta ranking of the Aspirational Districts combines the innovative use of data with pragmatic administration, keeping the district at the locus of inclusive development.

Design and implementation of an AI‐enabled visual report tool as formative assessment to promote learning achievement and self‐regulated learning: An experimental study

AbstractFormative assessment is essential for improving teaching and learning, and AI and visualization techniques provide great potential for its design and delivery. Using NLP, cognitive diagnostic and visualization techniques designed to analyse and present students' monthly exam data, we developed an AI‐enabled visual report tool comprising six modules and conducted an empirical study of its effectiveness in a high school biology classroom. A total of 125 students in a ninth‐grade biology course were assigned to a treatment group (n = 63) receiving AI‐enabled visual reports as the intervention and a control group (n = 62) receiving overall oral feedback from the teacher. We present the main statistical results of the within‐subjects design and the between‐subjects design respectively, to better capture the main findings. Repeated measures ANOVA revealed a significant interaction effect of intervention and time on learning achievement, and the paired‐sample Wilcoxon test indicated that the treatment group had experienced increasing learning anxiety (Cohen's d = 0.203, p = 0.046) and self‐efficacy (Cohen's d = 1.793, p = 0.000) over time. Moreover, we conducted a series of non‐parametric tests to compare the effects of AI‐enabled visual reports and teacher feedback, but found no significant differences except for an increased self‐efficacy (Cohen's d = 0.312, p = 0.046). Additionally, we had the students in the treatment group rate their favourable modules in the AI‐enabled visual report and provide evaluative feedback. The study results provide important insights into the design and implementation of effective formative assessment supported by artificial AI and visualization techniques. Practitioner notesWhat is already known about this topic Formative assessment is essential for improving teaching and learning. Traditional formative assessment tools lack accurate data‐oriented assessment and usability. AI and visualization techniques have great potential for formative assessment. What this paper adds This study designs and implements an AI‐enabled visual report tool that generates data‐driven, user‐friendly reports. The AI‐enabled visual report can not only enhance students' learning achievement and self‐regulated learning over time but also increase their test anxiety. The AI‐enabled visual report has a comparable effect with teacher feedback but leads to increased self‐efficacy. Implications for practice and/or policy We recommend using the AI‐enabled visual report in large‐size classes for its overall positive effects on both learning achievement and self‐regulated learning. We recommend using the AI‐enabled visual report over teacher feedback for its capacity to enhance students' self‐efficacy. We recommend prioritizing the modules of Performance Ranking, Personal Mastery and Knowledge Alert when designing the AI‐enabled visual report.

Improvement of Mobile Network Performance Ranking Using QoSKPI Index in Nigeria

This article introduces a novel approach to enhancing mobile network operator (MNO) performance ranking in Nigeria through the development and implementation of a Quality of Service Key Performance Indicator (QoSKPI) Index. As mobile technologies evolve and reliance on mobile services increases, the need for robust and comprehensible network performance metrics becomes critical. This study addresses gaps in current methodologies, which often assess Quality of Service (QoS) parameters independently, by aggregating multiple QoS Key Performance Indicators (KPIs) into a single comprehensive metric. By applying the Analytical Hierarchy Process (AHP), the research develops a QoSKPI Index that combines various network performance indicators into a unified ranking system. This index is validated against consumer complaints and empirical data to ensure its relevance and accuracy in reflecting the actual network performance experienced by users. Significant findings illustrate the utility of the QoSKPI Index in benchmarking MNOs' QoS performance, providing a clear empirical basis for ranking that can influence user choice and regulatory strategies aimed at enhancing network quality across Nigeria.

Oral health in lower middle-income African societies - surprising outcomes and strategic consequences

Objective: To assess the oral health status of the population in selected African countries and compare the outcomes with emerging and highly developed countries worldwide using quantifiable results. Method: By means of a composite indicator - the Dental Health Index (DHI) - the dental status of a country´s entire population is measured to arrive at a ranking of oral health performance. This performance is then compared with countries belonging to other development stages. The present study is descriptive and, whenever possible, uses existing epidemiological data from national representative surveys. Results: With the exception of Rwanda, the Sub-Saharan African countries performed better than the three North African countries of Egypt, Morocco and Tunisia, although the Arabic-speaking countries have higher Human Development Indices by comparison. Within Sub-Saharan Africa, the best rankings were achieved by Uganda, Kenya and Nigeria. However, the other Sub-Saharan countries studied also exhibited similarly low DHIs. Rwanda, Egypt, Tunisia and Morocco ranked last. One striking finding is that the Sub-Saharan countries studied show more favourable DHI outcomes than emerging or high-income countries. As large parts of the African population have been widely untouched for decades by dental services, the relatively favourable outcomes in African societies are difficult to explain and contradict conventional dental theory. The reasons behind this finding are not fully understood. It is only in the area of untreated oral diseases that the African countries perform much worse than the more developed countries. Conclusion: If African countries were to concentrate their scarce resources on a few vital segments of oral health, they would have the opportunity, in light of favourable morbidity structures, to gradually improve the oral health status of large parts of the population. Should they choose this route, priority should be given to the young generation. Three key action fields are decisive: mobilising oral self-care, enlarging the dental workforce by training sufficient mid-level personnel and creating a true primary oral health care system which is community-centred and complemented by strictly preventive, minimally invasive and tooth-retaining treatments under the guidance of a strong public sector.

A DEA-based decision framework for performance evaluation and ranking of workers in a real case from food industries

A DEA-based decision framework for performance evaluation and ranking of workers in a real case from food industries

Characteristics of Medical Quality in Tertiary Traditional Chinese Medicine Hospitals by TOPSIS and RSR Methods.

Performance evaluation is important for improving medical quality and services. But, there is a lack of research for medical quality in traditional Chinese medicine (TCM) hospitals. This study examines the medical quality and various indicators of tertiary public traditional Chinese medicine hospitals in Gansu Province, to establish a foundation for improving the medical and management standards of these hospitals. This study collected performance assessment data from 10 tertiary TCM hospitals in Gansu Province from 2019 to 2022. Thirteen indicators with TCM characteristics were selected and categorized into 3 aspects: control of medical costs, internal operational dimensions, and comprehensive management. The level of medical quality in different hospitals and in different years were determined using the TOPSIS method for ranking and the RSR method for grading. Firstly, in terms of TCM characteristic indicators, hospital H had the highest control of medical costs and comprehensive management among different hospitals, with 45.87% and 24.20% respectively. The highest values for control of medical costs and comprehensive management were observed in 2020, with 40.65% and 18.69% respectively among different years. When evaluating the medical quality of different hospitals using the TOPSIS method, it was found that hospital H had the highest ranking from 2020 to 2022, with Ci values of 0.725, 0.778, and 0.667 respectively. Additionally, the RSR method indicated that hospital H had a high level of grading from 2020 to 2022, with Pi values of 0.687, 0.690, and 0.723 respectively. These findings suggest that the medical quality of hospital H is at a high and stable level of development. Based on the TOPSIS method to evaluate the performance appraisal results and ranking of different hospitals from 2019 to 2022. The results showed that the highest ranking was hospital B(Ci = 0.913) in 2019. The highest ranking was hospital C(Ci = 0.809)in 2020. The highest ranking was hospital D(Ci = 0.689) in 2021. The highest ranking was hospital J(Ci = 0.865) in 2022. The RSR method indicated that high grading level were hospitals B(Pi = 0.899),F(Pi = 0.795) in 2019. The highest grading level was hospital C(Pi = 0.809) in 2020. The highest grading level were hospitals A(Pi = 0.868), D(Pi = 0.813), E(Pi = 0.689), G(Pi = 0.873), J(Pi = 0.813), K(Pi = 0.842) in 2022. Based on the above results indicate that there is a large variation in the medical quality profile of different hospitals from 2019 to 2022. By comparing the results of TOPSIS and RSR method from 2019 to 2022, we found that the hospitals with identical ranking were D and J, and the hospitals with ≤2 difference in ranking was A,B,C,E in 2019, the hospitals with >2 ranking was A, F in 2020, the hospitals with >2 ranking were C, G in 2021, and the hospitals with identical ranking results were B,D,E,G,J in 2022. Comparing the ranking results of TOPSIS and RSR methods, showed that the hospitals with identical rankings were B, F from 2019 to 2022. The difference in ranking results ≤2 were A, C, D, E, G, H, J, K, indicating that high consistency between TOPSIS and RSR methods and credible results. The findings reveal significant fluctuations in medical quality across different years, while the overall level of medical quality remains relatively stable among the various hospitals. It is recommended that TCM hospitals focus on improving management efficiency, optimizing hospital operations, enhancing the utilization of medical resources, and fostering the efficient development of hospitals.