Ranked Output Research Articles

Analysis and Evaluation of Harvest Quality Effectiveness of Carrot Clamping and Conveying Device

China’s carrot planting area and total output rank first in the world, but China’s mechanized carrot harvesting level is relatively backward. There are many problems in the existing machine operation process, among which the problems of a high leakage rate and high damage rate are the main difficulties faced. In order to study this problem, a test platform composed of clamping and pulling devices and conveying devices is designed, and it can complete the experiments of clamping, pulling and conveying carrot plants and collecting carrot stalks at one time. During the test, the clamping speed was divided into four test levels: 0.40 m/s (T1), 0.85 m/s (T2), 1.30 m/s (T3), and control test (CK), and each test level was carried out three times at different forward speeds. Finally, the leakage rate and damage rate were statistically analyzed. The results show that the average damage rate of Xiahong2 is 6.13%, 3.53%, and 9.36% and that of Sanhong is 6.22%, 3.76%, and 9.88% under the clamping and conveying speeds of T1, T2, and T3 in two years. The average carrot missed-pulling rate of two consecutive years corresponding to two carrot varieties, Xiaohong2 and Sanhong, was 3.68% and 4.14%, respectively. The carrot missed-pulling rate of CK in the control group of two carrot varieties, Xiaohong2 and Sanhong, was high and stable at 96.2% to 97.5%. At the same time, T1, T2, and T3 had similar overall trends of high carrot leakage rates for two carrot varieties at different clamping and conveying speeds. This control experiment also proves that the experimental arrangement is scientific and accurate. The average carrot leakage rate of T1, T2, and T3 for Xiahong2 is 3.91%, 3.42%, and 6.22%, and that of T1, T2, and T3 for Sanhong is 4.06%. The research results can provide a theoretical basis and reference for the optimization and improvement of carrot clamping and conveying devices, and this research can provide a reference for how to reduce the harvest loss of carrot combine harvesters in China.

Fuzzy normalization-based Multi-Attributive Border Approximation Area Comparison

In e-commerce, robust multi-criteria decision analysis is essential for accurately aligning rankings with customer preferences. Addressing this need, the Fuzzy Normalization-based Multi-Attributive Border Approximation Area Comparison (FN-MABAC) method introduces an advanced extension of the traditional MABAC model. The primary objective is to mitigate the ranking reversal paradox while enhancing decision-makers’ preference representation.To achieve these goals, FN-MABAC employs a fuzzy normalization approach, providing a structured framework that minimizes reversal risks and aligns with reference rankings. Experimental analyses compared FN-MABAC with established methods, including Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) and Characteristic Object METhod (COMET), using datasets on graphics card evaluations. Results demonstrate that FN-MABAC achieves higher concordance with reference rankings and exhibits superior resistance to ranking reversals compared to traditional approaches. Specifically, FN-MABAC achieved a Weighted Similarity (WS) coefficient match level of 0.97522 with the reference ranking, while STFN-MABAC reached a level of 0.99599, highlighting the high potential of these methods to adapt to preferences in the presented e-commerce example.Conclusions drawn indicate FN-MABAC’s potential to offer precise, stable rankings essential for e-commerce decision-making. Additionally, the concept of Stochastic Fuzzy Normalization-based MABAC (STFN-MABAC) is introduced as a re-identification approach, further aligning ranking outputs with customer preferences, particularly in scenarios with incomplete data. This study underscores FN-MABAC’s significance as an effective, resilient tool in multi-criteria analysis, contributing to improved decision-making within e-commerce contexts.

Sistem Pendukung Keputusan Seleksi Peserta Lomba Karnaval Murid TK Di Kabupaten Pidie Menggunakan Metode Multi Objective Optimization On The Basic Of Ratio Analysis (MOORA) Berbasis Web

Kindergarten is an example of an educational sector that has been impacted by advances in computer technology. One of the most important parts of a kindergarten is student assessment and the creativity of school children. In one kindergarten there are dozens of students and each has different creativity values. However, there are problems at the Pidie Regency Education Office in assessing the process of determining the selection as the best participant to take part in the kindergarten student carnival competition in Pidie Regency. This writing will discuss the design of a decision support system using the Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) method to select participants in the kindergarten student carnival competition in Pidie Regency. The aim of this research is to assist the Pidie District Education Office in making decisions by evaluating and ranking existing alternatives so that it is easier to determine which kindergarten student participants can take part in the carnival competition. The programming language used in designing this application is PHP using the CodeIgniter framework and HTML. For display using CSS3, namely using the Bootstrap and Jquery framework. The database uses MySQL. The tools and editors used are XAMPP for Windows, and Microsoft Visual Studio Code. The final result of this system is to provide a recommendation output ranking participants from the largest alternative value to the smallest. With this system, the Education Department will be helped and made easier in determining competition participants.

On preferences and reward policies over rankings

We study the rational preferences of agents participating in a mechanism whose outcome is a ranking (i.e., a weak order) among participants. We propose a set of self-interest axioms corresponding to different ways for participants to compare rankings. These axioms vary from minimal conditions that most participants can be expected to agree on, to more demanding requirements that apply to specific scenarios. Then, we analyze the theories that can be obtained by combining the previous axioms and characterize their mutual relationships, revealing a rich hierarchical structure. After this broad investigation on preferences over rankings, we consider the case where the mechanism can distribute a fixed monetary reward to the participants in a fair way (that is, depending only on the anonymized output ranking). We show that such mechanisms can induce specific classes of preferences by suitably choosing the assigned rewards, even in the absence of tie breaking.

Robust low-rank learning multi-output regression for incipient sediment motion in sewer pipes

The existing incipient sediment motion models typically apply conventional regression methods considering either velocity or shear stress. In the current study, incipient sediment motion is analyzed through a simultaneous and joint analysis of velocity and shear stress using the robust low-rank learning (RLRL) multi-output regression technique. Moreover, the experimental data compiled from five different channels are utilized to develop a generic incipient sediment motion model valid for a channel of any cross-sectional shape. The efficiency of the developed method is examined and compared against the available conventional regression models. The experimental results indicate that the RLRL model yields better results than its counterparts. In particular, while cross-section specific models fail to provide accurate estimates for shear stress or velocity for other cross sections, the proposed model provides satisfactory results for all channel shapes. The better performance of the recommended approach can be attributed to the joint modeling of the shear stress and the velocity which is realized by capturing the correlation between these parameters in terms of a low rank output mixing matrix which enhances the prediction performance of the approach.

Validation of reference gene stability for miRNA quantification by reverse transcription quantitative PCR in the peripheral blood of patients with COVID-19 critical illness.

The COVID-19 pandemic has created an urgency to study the host gene response that leads to variable clinical presentations of the disease, particularly the critical illness response. miRNAs have been implicated in the mechanism of host immune dysregulation and thus hold potential as biomarkers and/or therapeutic agents with clinical application. Hence, further analyses of their altered expression in COVID-19 is warranted. An important basis for this is identifying appropriate reference genes for high quality expression analysis studies. In the current report, NanoString technology was used to study the expression of 798 miRNAs in the peripheral blood of 24 critically ill patients, 12 had COVID-19 and 12 were COVID-19 negative. A list of potentially stable candidate reference genes was generated that included ten miRNAs. The top six were analyzed using reverse transcription quantitative polymerase chain reaction (RT-qPCR) in a total of 41 patients so as to apply standard computational algorithms for validating reference genes, namely geNorm, NormFinder, BestKeeper and RefFinder. There was general agreement among all four algorithms in the ranking of four stable miRNAs: miR-186-5p, miR-148b-3p, miR-194-5p and miR-448. A detailed analysis of their output rankings led to the conclusion that miR-186-5p and miR-148b-3p are appropriate reference genes for miRNA expression studies using PaxGene tubes in the peripheral blood of patients critically ill with COVID-19 disease.

THE EFFECT OF LEARNING MODELS (TPS, TPSq, CONVENSIONAL) AND STUDENTS' TAHSIN ABILITY ON PAI LEARNING OUTCOMES

The cooperative learning model can be supported in the PAI learning process. This study analyzed the effect of learning models (TPS, TPSq, and Conventional) and students' abilities on Islamic education learning outcomes. This research method uses a quantitative type, with the Nonequivalent Posttest_Only Control Group design, with statistical test analysis using Two Way Anova. The results of the analysis of this study indicate that there are differences in learning models (TPS, TPSq, and Conventional) and students' Tahsin abilities in PAI learning outcomes; it can be seen from the output rank that the TPS learning model is 17.65 less than the mean rank of students in the class model. Learning TPSq is 31.35 (17.65 31.35). The mean rank value of students in the TPSq learning model class is 32.35, more significant than that of students in the conventional learning model class, which is 16.65 (32.35 16.65). The mean rank value of students in the TPS learning model class is 26.96, more significant than that of students in the Conv learning model class, which is 22.04 (26.96 22.04). There are differences in Islamic education learning outcomes for class VII A and B students based on Tahsin’s ability; based on the Statistical Test results, the Sig.2-tailed is 0.000, which is more minor () than 0.05. There are differences in the learning outcomes of students in classes VII B and C based on Tahsin’s abilities; based on the results of the Test Statistics, the value of Sig.2-tailed is 0.006, this value is more minor ( ) than 0.05, and there are differences in the learning outcomes of students in class VII A and C. based on Tahsin ability, based on the results of the Statistical Test the value of Sig.2-tailed is 0.000 this value is more minor () than 0.05.

Multi-dimensional Bayesian network classifiers for partial label ranking

The label ranking problem consists in learning preference models from training datasets labeled with (possibly incomplete) rankings of the class labels. The goal is then to predict a ranking for a given unlabeled instance. This work focuses on a more general interpretation where both the training dataset and the prediction given as output allow tied class labels, i.e., there is no particular preference between them. This problem is known as the partial label ranking problem. This paper tackles the partial label ranking problem by transforming the ranking with ties into a set of discrete variables representing the preference relations (ranked ahead, tied with, and ranked behind) between each pair of class labels. The posterior probabilities for each pair are then used to fill the values of a preference matrix. This preference matrix is the basis for solving the rank aggregation problem required to obtain the output ranking with ties. This paper aims to exploit the resemblance of this problem with multi-label and multi-dimensional classification by studying the use of Bayesian network classifiers to compute the posterior probabilities for the new class structure, i.e., pairs of class labels. In particular, binary relevance with naive Bayes and averaged one-dependence estimators between the new class structure are used to solve the partial label ranking problem. Furthermore, bivariate relationships between all the pairs of class labels are considered. However, the complexity of the model grows significantly, which makes it necessary to reduce the number of allowed bivariate relationships between pairs. Thus, a feature selection method is included to select the more relevant subset of bivariate relationships. The experimental evaluation shows that our proposals are competitive in accuracy with the current instance-based and decision tree induction algorithms. Moreover, they outperform the existing mixture-based probabilistic graphical models, while the algorithms proposed are much faster.

Neurosurgery residency rankings: An assessment of Doximity’s survey-based data and objective program characteristics

BackgroundMedical students often consult residency program reputation rankings whilst creating residency program rank lists. Accurate assessment of a residency program remains a cumbersome task, primarily due to varying degrees of interpretation and bias. The Doximity Residency Navigator (DRN) is a tool used to assist medical students in making informed residency decisions whilst simultaneously increasing transparency in the residency match process. DRN is comprised of data that assist applicants research prospective training programs, including reputation rankings across specialties, resident and alumni satisfaction surveys, and objective statistics such as training program size and research output. ObjectiveThis study aims to evaluate the correlation between different Doximity rankings across neurosurgical residency programs. MethodsRankings and the relevant criteria (reputation, research output, and percentage of graduates that subspecialize) and program size for all neurological surgery residency programs in the United States were obtained from the 2020–2021 Doximity Residency Navigator. Independent correlation analyses were run to ascertain the relationship between reputation ranking and 1) program size, 2) research output ranking and 3) ranking of the percentage of graduates who subspecialize. ResultsThe reputation rankings of neurosurgical residency programs are determined, in part, by nomination surveys completed by board-certified physicians. A statistically significant correlation was observed between reputation ranking and 1) program size and 2) research output ranking. ConclusionReputation rankings of neurosurgical residency programs put forth by Doximity Inc. incorporate reputational data and other objective outcome criteria. A nuanced understanding of the Doximity rankings will facilitate its use by medical students and healthcare professionals.

KINERJA KEPALA SEKOLAH DAN KINERJA MENGAJAR GURU DALAM PENINGKATAN MUTU SEKOLAH DASAR NEGERI DI KECAMATAN KOTA TANGERANG

This study aims to determine the effect of principal performance (X1) and teacher performance (X2) on school quality (Y). The method used in this research is descriptive method with a quantitative approach with the study population consisting of 85 public elementary schools in Tangerang district. The total sample of 46 schools was taken based on proportionate stratified random sampling from all public elementary schools in the Tangerang city district to obtain data on school quality. The results of the descriptive analysis show that the school quality variable gets a score of 3.70 which is in the high category. This means that the quality of public elementary schools in the sub-district of Tangerang city is good. The dimension that gets the highest score is outcame at the first level, then the second input, the third enhanced process, and the last ranked output. Meanwhile, the school principal's performance variable obtained a score of 4.32 and was in the very high category. This means that the performance of school principals in the Tangerang sub-district has been very good. The dimension that gets the highest score is initiative at the first level, then the second is quality of work and capability, the third is improved promptness, and the last is communication. Then, the results of data processing on the teacher's teaching performance variable obtained a score of 4.59 and were in the very high category. This means that the teaching performance of teachers in the district of Tangerang city is very good. The dimension that gets the highest score is planning at the first level, then the second implementation, the final enhanced assessment.

Ensemble feature selection using q-rung orthopair hesitant fuzzy multi criteria decision making extended to VIKOR

ABSTRACT This paper investigates ensemble feature selection using the q-rung orthopair hesitant fuzzy multi-criteria decision-making (MCDM) process. A novel algorithm is proposed for the study of ensemble feature selection and it is called as q-rung orthopair hesitant fuzzy MCDM extended to the Visekriterijumska optimizacija Ikompromisno Resenje (VIKOR) (q-ROHFS VIKOR). This is the first time in the literature an ensemble feature selection problem is modelled as a q-rung orthopair hesitant fuzzy MCDM extended to VIKOR technique. In the proposed method, every feature is ranked based on different available rankers and a preference matrix is obtained. In the sigmoidal transformation, the tuning parameter plays a vital role in the fuzzification process. This tuning parameter reduces the computation run-time in the fuzzification process for different high-dimensional datasets that are considered in this paper. By using q-ROHFS VIKOR method, a score is assigned to each feature based on the values of the preference matrix. At last, an output rank vector is produced for all features from which the user can select the desired number of features. To prove the efficiency and optimality of the proposed method, the comparison with basic filter-based feature selections and ensemble feature selection using feature ranking strategy is obtained. The proposed method in this paper is superior and efficient than the ensemble methods based on the accuracy and F-score levels upto 0.96.

Nationwide Bibliometric Analysis of Integrated Plastic Surgery Program Directors.

We identified plastic surgery programs based on 2021 Doximity reputation and research output rankings, respectively, and then divided them into four quartiles (Q1-Q4). PD academic history and bibliometric indices (h-index, the number of publications, and citations) were collected through Doximity profiles and program websites: PubMed, Scopus, Google Scholar, American Society of Plastic Surgeons, and Accreditation Council for Graduate Medical Education. Eighty-four programs were identified. There was a significant positive relationship between h-index, the number of publications, and type of research with reputation ranking (P < 0.05). After adjusting for years of experience post-training, h-index (OR = 1.24; P < 0.001) and the number of publications (OR = 1.05, P < 0.001) were significantly associated with reputation ranking. There was a statistically significant relationship between PD research fellowship completion and research output ranking (P < 0.01). After adjusting for years of experience post-training, h-index (OR = 1.05; P = 0.047) and the number of publications (OR = 1.01; P = 0.04) were significantly associated with research output ranking. Higher ranked programs tend to have PDs who have a strong record of scholarly activity, as evidenced by certain bibliometric indices.

A multistage retrieval system for health-related misinformation detection

Web search is widely used to find online medical advice. As such, health-related information access requires retrieval algorithms capable of promoting reliable documents and filtering out unreliable ones. To this end, different types of components, such as query-document matching features, passage relevance estimation and AI-based reliability estimators, need to be combined. In this paper, we propose an entire pipeline for misinformation detection, based on the fusion of multiple content-based features. We present experiments which study the influence of each pipeline stage for the target task.Our technological solution incorporates signals from technologies derived from diverse research fields, including search, deep learning for natural language processing, as well as advanced supervised and unsupervised learning. To combine evidence, different score fusion strategies are compared, including unsupervised rank fusion techniques and learning-to-rank methods. The reference framework for empirically validating our solution is the TREC Health Misinformation Track, which provides several challenging subtasks that foster research on the identification of reliable and correct information for health-related decision making tasks. More specifically, we address a total recall task, the goal of which is to identify all the documents conveying incorrect information for a specific set of topics, and an ad-hoc retrieval task, aiming to rank credible and correct information over incorrect information. All variants are evaluated with an assorted set of effectiveness metrics, which includes standard search measures, such as R-Precision, Average Precision or Normalised Discounted Cumulative Gain, and innovative metrics based on the compatibility between the ranked output and two reference rankings composed of helpful and harmful documents, respectively.Our experiments demonstrate the effectiveness of the proposed pipeline stages and indicate that sophisticated supervised fusion methods do not fare better than simpler fusion alternatives. Additionally, for reliability estimation, unsupervised textual similarity performs better than textual classification based on supervised learning. The results also show that the presented approach is highly competitive when compared with state-of-the-art solutions for the same problem.

A Multimodal Retrieval and Ranking Method for Scientific Documents Based on HFS and XLNet

Aiming at the defects of traditional full-text retrieval models in dealing with mathematical expressions, which are special objects different from ordinary texts, a multimodal retrieval and ranking method for scientific documents based on hesitant fuzzy sets (HFS) and XLNet is proposed. This method integrates multimodal information, such as mathematical expression images and context text, as keywords to realize the retrieval of scientific documents. In the image modal, the images of mathematical expressions are recognized, and the hesitancy fuzzy set theory is introduced to calculate the hesitancy fuzzy similarity between mathematical query expressions and the mathematical expressions in candidate scientific documents. Meanwhile, in the text mode, XLNet is used to generate word vectors of the mathematical expression context to obtain the similarity between the query text and the mathematical expression context of the candidate scientific documents. Finally, the multimodal evaluation is integrated, and the hesitation fuzzy set is constructed at the document level to obtain the final scores of the scientific documents and corresponding ranked output. The experimental results show that the recall and precision of this method are 0.774 and 0.663 on the NTCIR dataset, respectively, and the average normalized discounted cumulative gain (NDCG) value of the top-10 ranking results is 0.880 on the Chinese scientific document (CSD) dataset.

An integrated optimal method for cloud service ranking

Many cloud providers present various services with different attributes. It is a complex, lengthy process to select a cloud service that meets user requirements from an assortment of services. At the same time, user requirements are sometimes defined with imprecision (sets or intervals), whereas it is also important to consider the quality of user feedback (QoU) and quality of service (QoS) attributes for ranking. Besides, each MADM method has a di erent procedure, which causes functional contradictions. These contradictions have led to confusion in choosing the best MADM method. It is necessary to provide a method that harmonizes the results. Therefore, choosing a method for ranking cloud services that addresses these issues is currently a challenge. This paper proposes an optimal cloud service ranking (OCSR) method that ranks cloud services efficiently based on imprecise user requirements in both QoS and QoU aspects. OCSR consists of four stages including receiving the requirements, preprocessing, ranking, and integrating the ranking results. At the receiving requirements stage, the query format is created. In the preprocessing stage, a requirement interval is created for considering imprecise user requirements in order to filter inappropriate services. Based on QoS and QoU attributes, cloud services are then ranked through multiple multi-attribute decision-making (multi-MADM) methods such as the prominent MADM techniques. Finally, the ranking outputs of various methods are integrated to obtain the optimal results. The experimental results confirm that the OCSR outperforms the previous methods in terms of optimality of ranking, sensitivity analyses, and scalability.

A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis.

Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), an unsupervised learning method that projects the inputs onto a common subspace so as to maximize the correlations between the projections. To this end, we seek a multichannel CCA algorithm that can be implemented in a biologically plausible neural network. For biological plausibility, we require that the network operates in the online setting and its synaptic update rules are local. Starting from a novel CCA objective function, we derive an online optimization algorithm whose optimization steps can be implemented in a single-layer neural network with multicompartmental neurons and local non-Hebbian learning rules. We also derive an extension of our online CCA algorithm with adaptive output rank and output whitening. Interestingly, the extension maps onto a neural network whose neural architecture and synaptic updates resemble neural circuitry and non-Hebbian plasticity observed in the cortex.

Beyond pairwise comparisons in social choice: A setwise Kemeny aggregation problem

In this paper, we advocate the use of setwise contests for aggregating a set of input rankings into an output ranking. We propose a generalization of the Kemeny rule where one minimizes the number of k-wise disagreements instead of pairwise disagreements (one counts 1 disagreement each time the top choice in a subset of alternatives of cardinality at most k differs between an input ranking and the output ranking). After an algorithmic study of this k-wise Kemeny aggregation problem, we introduce a k-wise counterpart of the majority graph. This graph reveals useful to divide the aggregation problem into several sub-problems, which enables to speed up the exact computation of a consensus ranking. By introducing a k-wise counterpart of the Spearman distance, we also provide a 2-approximation algorithm for the k-wise Kemeny aggregation problem. We conclude with numerical tests.

Pengembangan Perangkat Pembelajaran Evaluasi IPA Biologi Berbasis Digital Untuk Meningkatkan Kompetensi Keguruan Bagi Mahasiswa UNIMED

The development of information technology encourages the Teaching Faculty to produce Pre-service graduates who master professional and pedagogical competences enhanced by Information Communication Technology skills. This development research aims to develop evaluation instruments for digital-based education courses, especially evaluation. The ADDIE model is implemented to compile lesson plans and digital-based evaluation instruments. The research was conducted in five stages, namely the stages of analysis, material design, development, and implementation. The results of implementing digital-based evaluation instruments show efficiency and effectiveness for lecturers because the kahoot application provides direct reports for feedback and respondents are very motivated to answer the next point better and accurately. Respondents were impressed by the kahoot application with text, images, time allocation and also music, it was more attractive for respondents to focus more and think faster. Likewise with google form s with its efficiency and effectiveness for students and lecturers because it immediately reports immediately after the complete answer is submitted along with the output ranking. This research implies the importance of considering the use of digital-based evaluation instruments for better, more efficient, more effective feedback and follow-up for future evaluation courses.

Voting with random classifiers (VORACE): theoretical and experimental analysis

In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.

Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment

We consider the issue of strategic behaviour in various peer-assessment tasks, including peer grading of exams or homeworks and peer review in hiring or promotions. When a peer-assessment task is competitive (e.g., when students are graded on a curve), agents may be incentivized to misreport evaluations in order to improve their own final standing. Our focus is on designing methods for detection of such manipulations. Specifically, we consider a setting in which agents evaluate a subset of their peers and output rankings that are later aggregated to form a final ordering. In this paper, we investigate a statistical framework for this problem and design a principled test for detecting strategic behaviour. We prove that our test has strong false alarm guarantees and evaluate its detection ability in practical settings. For this, we design and conduct an experiment that elicits strategic behaviour from subjects and release a dataset of patterns of strategic behaviour that may be of independent interest. We use this data to run a series of real and semi-synthetic evaluations that reveal a strong detection power of our test.