Multiple Ranking Research Articles

Quantitative characterization of geomorphological and topographical features of debris-flow channels at the Alpe di Succiso mountain, Northern Apennines (Italy)

ABSTRACT This study combines the traditional method of geological and geomorphological mapping with a quantitative GIS analysis to examine landforms and debris flows channels. Using a high-resolution DEM (5 m) as a base, the geomorphological map highlights the various landforms around the Alpe di Succiso (Northern Apennines, Italy), including glacial and cryoclastic features, slope landforms and deposits due to gravity (particularly debris flow) and deposits due to running waters. The methodology applied to the channels is based on the geomorphometric parameters derived from the DEM, including Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), Slope Angle, and Elevation extracted every 10 m along each channel and grouping the channels in four classes based on their lengths. Within each class, multiple ranking criteria were employed for the identification of channels showing anomalous or common patterns. Debris flow channels exhibit anomalous patterns in the geomorphometric parameters, indicating significant variations in their topographical characteristics..

The Influence of Open Access on Institutional Scientific Research Output in MENA Countries: A Comparative Analysis

The release of scientific research has substantial consequences for both the research and the institution in which it was carried out. The selection of publishing methods for scientific articles, such as journal type, quartile, indexed databases, and publishing models, plays a crucial role. Various publishing models exist, including platinum, gold, and green open access. In this study, we examine the implications of open access publishing on reputable journals for Middle East and North Africa (MENA) countries. Additionally, we explore the correlation between open access publishing and MENA country classification according to the World Bank. Bibliometric data from Scopus database are used, focusing on researchers from each MENA country. The World Bank determines if a country is low, middle, or high income. The total number of published documents and open access publications are also considered. The study investigates how open access publishing affects university and research center rankings within MENA countries, utilizing Scopus database information. This encompasses the number of open access documents indexed in Scopus and citations from MENA countries. Multiple ranking systems, such as 2024-QS World University Ranking (QSWUR) and 2023-Scimago Institutions Ranking, are assessed. Findings reveal that open access articles contribute to 72% to 95% of citations received by institutions in MENA countries, highlighting the influence of this publishing model. Additionally, the percentage of open access publishing ranges from 32% in low-income countries to 56% in other MENA countries. The study also demonstrates that MENA countries with higher numbers of open access publications are more likely to have institutions ranked in prestigious systems such as QS World University Ranking and Scimago Institutions. It is discovered that the average percentage of citations received from open access publications indexed in Scopus for MENA countries is 82%, indicating the significant impact of open access publishing on academic citations in these nations. We believe that the procedures done in this study for MENA countries can be generalized further for global countries.

Low-rank sparse fully-connected tensor network for tensor completion

Fully-connected tensor network (FCTN) has recently drawn lots of attention in tensor completion due to its full description of all correlations between any two modes. However, the FCTN model has multiple ranks, and existing methods often ignore the difficulty brought about by rank selection, especially when the model is rank-sensitive. To overcome this drawback, this paper proposed a low-rank sparse FCTN for tensor completion. Specifically, we theoretically show that, for a tensor with FCTN structure, its subtensor can be represented as a coefficient sum of basic tensors, where the coefficients are remained in the FCTN’s factors. This means that factors’ sparsity can improve the robustness of FCTN to larger rank selection. Moreover, according to the relation between the target tensor and its FCTN’s factors, low-rank constrain is used to enhance rank robustness. Lastly, we optimize the proposed model by the alternating direction method of multipliers (ADMM) algorithm. Experimental results show that the low-rank sparse constraint effectively improves the rank robustness of the FCTN model, and achieves excellent results compared with other state-of-the-art completion methods.

Reduced time to efficacy signals in advanced cancer trials using Chauhan weighted trajectory analysis.

e23028 Background: As Kaplan-Meier (KM) analyzes single unidirectional endpoints, most advanced cancer randomized clinical trials (RCTs) are powered for a 10 endpoint of either progression free survival (PFS) or overall survival (OS). This disregards efficacy information carried by stable disease (SD), partial response (PR), and complete response (CR) that may precede progressive disease (PD) and death. Chauhan Weighted Trajectory Analysis (CWTA), a generalization of KM, permits simultaneous evaluation of multiple rank ordered endpoints. We hypothesized that using CWTA with all efficacy inputs could expedite RCT efficacy signals. Methods: We performed 100-fold simulations of RCTs in advanced cancer and determined the time to first significant efficacy signal (p < 0.05) for KM PFS and OS (logrank test) and CWTA (weighted logrank test). RCTs were stochastically generated at defined sample size (SS) allocated 1:1 to control or intervention and run for 60 months. Rank ordered health statuses were CR = 0, PR = 1, SD = 2, PD = 3, and Death = 4. All patients we assigned SD at time 0 and, each month, were capable of response (PR then CR), maintained SD, or irreversible exacerbation to PD or Death. Event probabilities were modified between groups as defined by a hazard ratio (HR). We modeled a control group CR rate of ~10% and a PR rate of ~50% to reflect first-line advanced cancer RCTs with a dropout rate of 10% over 60 months. At increments of SS and HR, we determined the mean and standard deviation of time-to-efficacy signals. Results: CWTA markedly reduced the time-to-efficacy signals when compared to KM PFS (23% to 67%) and KM OS (43% to 82%) (Table 1). Conclusions: CWTA, by incorporating the entirety of the cancer trajectory including disease response, progression, and death, meaningfully expedites the efficacy signals of cancer treatments compared to KM PFS and OS. We have previously reported CWTA increases trial power and reduces sample size requirements (Chauhan U, Zhao K, Walker J, Mackey JR; 2023. DOI: 10.3390/biomedinformatics3040052). Using CWTA rather than KM reduces trial duration, cost, and numbers of patients needed to evaluate therapies in advanced cancer, while reducing the regulatory risk inherent in powering a trial for a single KM primary endpoint. [Table: see text]

HBCR_DMR: A Hybrid Method Based on Beta-Binomial Bayesian Hierarchical Model and Combination of Ranking Method to Detect Differential Methylation Regions in Bisulfite Sequencing Data.

DNA methylation is a key epigenetic modification involved in gene regulation, contributing to both physiological and pathological conditions. For a more profound comprehension, it is essential to conduct a precise comparison of DNA methylation patterns between sample groups that represent distinct statuses. Analysis of differentially methylated regions (DMRs) using computational approaches can help uncover the precise relationships between these phenomena. This paper describes a hybrid model that combines the beta-binomial Bayesian hierarchical model with a combination of ranking methods known as HBCR_DMR. During the initial phase, we model the actual methylation proportions of the CpG sites (CpGs) within the replicates. This modeling is achieved through beta-binomial distribution, with parameters set by a group mean and a dispersion parameter. During the second stage, we establish the selection of distinguishing CpG sites based on their methylation status, employing multiple ranking techniques. Finally, we combine the ranking lists of differentially methylated CpG sites through a voting system. Our analyses, encompassing simulations and real data, reveal outstanding performance metrics, including a sensitivity of 0.72, specificity of 0.89, and an F1 score of 0.76, yielding an overall accuracy of 0.82 and an AUC of 0.94. These findings underscore HBCR_DMR's robust capacity to distinguish methylated regions, confirming its utility as a valuable tool for DNA methylation analysis.

QI-IRA: Quantum-Inspired Interactive Ranking Aggregation for Person Re-identification

Ranking aggregation (RA), the process of aggregating multiple rankings derived from multiple search strategies, has been proved effective in person re-identification (re-ID) because of a single re-ID method can not always achieve consistent superiority for different scenarios. Existing RA research mainly focus on unsupervised and fully-supervised methods. The former lack external supervision to optimize performance, while the latter are costly because of expensive labeling effort required for training. To address the above challenges, this paper proposes a quantum-inspired interactive ranking aggregation (QI-IRA) method, which (1) utilizes quantum theory to interpret and model the generation and aggregation of multiple basic rankings, (2) approximates or even exceeds the performance of fully-supervised RA methods with much less labeling cost, even as low as only two feedbacks per query on Market1501, MARS and DukeMTMC-VideoReID datasets. Comparative experiments conducted on six public re-ID datasets validate the superiority of the proposed QI-IRA method over existing unsupervised, interactive, and fully-supervised RA approaches.

ROSELLE CALYX (HIBISCUS SABDARIFFA. L) AS AN ANTI-DIABETIC: ETHYL ACETATE FRACTION REDUCE FASTING BLOOD GLUCOSE TOTAL CHOLESTEROL AND REPAIR PANCREAS FUNCTION ON DIABETIC MODEL

Objective: In general, people with diabetes mellitus will experience disturbances in fat metabolism that lead to hypercholesterolemia. This study aims to determine the effect of ethyl acetate fraction hibiscus calyx on blood sugar, blood cholesterol level, and pancreas histology in diabetic Wistar Kyoto rats induced by streptozotocin.
 Methods: Thirty-six Wistar Kyoto rats were induced with intra-peritoneal streptozotocin at 55 mg/kg BW and stabilized for five days to obtain diabetic conditions. Diabetic animals were divided into four groups; the diabetic group was given vehicle, the glibenclamide group was given 0.45 mg/kg BW of Glibenclamide, and two groups were administered the ethyl acetate fraction of hibiscus calyxes (EAFHC) at doses of 100 mg/kg BW and 200 mg/kg BW for five days. Fasting blood sugar and lipids (total cholesterol and triglycerides) were measured on days 0, 1, 3, and 5. Pancreats were collected on days 1, 3 and 5 for weighing and histology examination. All data were analyzed using two-way ANOVA followed by the Duncan Multiple Rank Test (DMRT).
 Results: EAFHC significantly reduced fasting blood sugar and total cholesterol (p<0.05) but did not have a significant effect on triglycerides (p>0.05). Histology examination showed that EAFHC repaired pancreatic damage, as seen from the decrease in pancreatic histology scores (p<0.05), but the organ ratio did not show a significant improvement (p>0.1). 
 Conclusion: This study revealed that EAFHC could be an alternative medicine in managing blood sugar levels and total cholesterol and improving pancreas function in associated models of diabetes mellitus hypercholesterolemia complications.

A unifying rank aggregation framework to suitably and efficiently aggregate any kind of rankings

The aggregation of multiple rankings into a consensus ranking is a crucial task in various domains such as search engine results or user-based ratings. This task poses significant challenges due to its inherent complexity. The complexity of the problem stems not only from the need for exactness and efficiency, but also from the diversity of real-world scenarios, which often involve incomplete rankings and ties.Most existing methods propose a specific way to aggregate rankings. However, these methods often do not take into account different real use-case scenarios which can impact the relevance of their final result, as the congruence between the aggregated output and the expected outcome inherently depends on the context. To address the issue of context-dependency in ranking aggregation, we introduce a unifying framework that subsumes a variety of generalizations of the Kemeny score for incomplete rankings with ties and enables the design of new ones if a specific context requires it. Our framework is parameterized, allowing for different behaviors depending on the specific use case.We provide a broader scope of application to the methods encompassed by our approach, augmenting them with a larger theoretical and algorithmic structure. We establish an axiomatic study to better understand each method within our framework and present an algorithmic approach that includes exact methods, partitioning algorithms, and heuristics.Finally, we demonstrate the practical relevance of our approach through an empirical study on both real and synthetic datasets. Notably, the synthetic datasets are generated based on devised real-world scenarios, highlighting the context-dependent applicability of different Kemeny-based rank aggregation methods within our framework.

Crowdsourcing Pleasantness and Safety Perceptions: An Analysis through Multiple Rankings and Socio-Demographic Groups

ABSTRACT Our perception of public spaces guides our behavior on them. Understanding which factors shape this perception informs both urban planners, who aim to improve city life, as well as computational models that help us navigate urban spaces. Crowdsourcing games have been employed to evaluate citizens’ perceptions of urban scenes at scale. This work contributes to crowdsourcing studies by evaluating different strategies to aggregate perceptions and rank scenes, as well as investigating the relation of captured perceptions with features of the urban form and the socio-demographic profile of participants.

Optimizing Convolutional Neural Networks Utilizing Tensor Decomposition Techniques for Large-Scale Image Recognition Tasks

Convolutional Neural Networks (CNNs) are integral to numerous applications in today's digital landscape. However, their computational demands often result in slow operation, especially when resources are constrained. This study introduces two tensor decomposition methods aimed at optimizing the performance of CNNs by minimizing the total number of operations and weights, while maintaining accuracy. The first method employs Canonical Polyadic (CP) decomposition to divide the convolutional layer into multiple rank 1 tensors. The second method uses Tucker decomposition to break down the convolutional layer into a compact core tensor and several matrices. The effectiveness of these methods was evaluated on widely-used convolutional architectures, VGG16 and LeNet, by substituting their convolutional layers with a series of decomposed layers, implemented using PyTorch and TensorLy. The CP decomposition method achieved a computational speed-up of 43% with a minimal accuracy reduction of less than 0.12%. Similarly, Tucker decomposition resulted in a 37% speed-up with an accuracy decrease of less than 0.16%. These findings suggest that the proposed tensor decomposition methods can significantly enhance the efficiency of CNNs without significantly impacting their performance.

Rank 1 perturbations in random matrix theory — A review of exact results

A number of random matrix ensembles permitting exact determination of their eigenvalue and eigenvector statistics maintain this property under a rank [Formula: see text] perturbation. Considered in this review are the additive rank [Formula: see text] perturbation of the Hermitian Gaussian ensembles, the multiplicative rank [Formula: see text] perturbation of the Wishart ensembles, and rank [Formula: see text] perturbations of Hermitian and unitary matrices giving rise to a two-dimensional support for the eigenvalues. The focus throughout is on exact formulas, which are typically the result of various integrable structures. The simplest is that of a determinantal point process, with others relating to partial differential equations implied by a formulation in terms of certain random tridiagonal matrices. Attention is also given to eigenvector overlaps in the setting of a rank [Formula: see text] perturbation.

Analysis of Ordinal Populations from Judgment Post-Stratification

In surveys requiring cost efficiency, such as medical research, measuring the variable of interest (e.g., disease status) is expensive and/or time-consuming; however, we often have access to easily obtainable characteristics about sampling units. These characteristics are not typically employed in the data collection process. Judgment post-stratification (JPS) sampling enables us to supplement the random samples from the population of interest with these characteristics as ranking information. This paper develops methods based on the JPS samples for estimating categorical ordinal populations. We develop various estimators from the JPS data even for situations where the JPS suffers from empty strata. We also propose the JPS estimators using multiple ranking resources. Through extensive numerical studies, we evaluate the performance of the methods in estimating the population. Finally, the developed estimation methods are applied to bone mineral data to estimate the bone disorder status of women aged 50 and older.

Analytic hierarchy process and data envelopment analysis: A match made in heaven

Analytic hierarchy process (AHP) and data envelopment analysis (DEA) are two popular decision science methods with many business, science, and engineering applications. AHP is a Multi-Attribute Decision-Making (MADM) method for prioritizing alternatives, and DEA is a non-parametric method for estimating production frontiers. Each method has known strengths and weaknesses, and the strengths in one method can overcome the weaknesses in the other. We study several strategies to diminish the weaknesses of DEA with the strengths of pairwise comparisons in AHP. We tackle the low discrimination power inherent to conventional DEA methods. AHP, with its pairwise comparison capability, has been consistently used to increase the discrimination power and accuracy in DEA. We propose and evaluate several new hybrid MADM-DEA models of different computational complexity and consistency, including combinations of the best-worst method (BWM) and its variants with DEA as well as a novel method composed of Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) and DEA. We further develop a new technique for evaluating the similarity among multiple ranking results in MADM. The new simple but powerful technique is called Rank Absolute Deviation (RAD) and is inspired by the mean absolute deviation method. Several numerical examples and a real-world problem are used to demonstrate the applicability and efficacy of the new BWM-DEA, MACBETH-DEA, and RAD methods proposed in this study. We illustrate how less computationally demanding MADM-DEA techniques provide rankings that are highly correlated with the benchmark DEA-AHP and different consensus ranking models.

Grammatical Polymorphism Between Consideration and Inference


 
 
 
 ABSTRACT
 
 The grammar of the Arabic Language has received the attention of its scholars .They were interested in reclining the grammatical rule and install the pillars of these bases which made them take care of the order of its sentences until they became readymade templates among the scholars .It is covered in a later lesson from an earlier one . It was among their cocern with those rules, their attention to multiple ranks in one sentence to give this multiplicity an addition benefit that didant exist in the sentence without plurality. This multiplicity was the intention of the Arabic speaker to lead to use on the one hand and a mural benefit deroid of the Arabic sentence if there is no multiplicity in it.
 
 
 

Efficacy of surgical periodontal treatment with and without photobiomodulation in the treatment of severe periodontitis: An evaluation of periodontal, microbiological, and cytokine levels

AimTo compare the efficacy of surgical periodontal treatment (SPT) alone and PDT-assisted surgery in participants with severe periodontitis. Material and methodsThe present clinical trial was completed by 64 participants (n = 32 each). The selection was made according to predefined inclusion and exclusion criteria. Patients in group A were treated with SPT only and participants in group B were treated with SPT adjunct to PDT. Microbiological assessment of P.Gingivalis; T. Forsythia and T.Denticola were evaluated using cultural analysis and periodontal parameters plaque score (PSc), bleeding on probing (BoP) periodontal depth (PD), and clinical attachment loss (CAL) at baseline and post-treatment at 6 months and 12 months were performed. The gingival crevicular fluid (GCF) was collected for the estimation of IL-1β and tumor necrosis factor-alpha (TNF-α) using an enzyme-linked immunosorbent assay (ELISA). For intra-group comparison and post hoc correction, Student's t-test along with Bonferroni was used. For the difference between follow-ups, an analysis of variance (ANOVA) multiple rank tests were incorporated. ResultsThe mean age of participants in the SPT group was 55.25±4.6yrs. Whereas, participants treated with PDT adjunct to SPT were 54.88±3.6yrs. Periodontal parameters (BoP, PD, PSc, CAL) showed no significant difference at baseline. At 6 months and 12 months follow-up, a significant difference in all parameters (BoP, PD, PSc, and CAL) was found in participants treated with SPT alone and PDT adjunct to SPT (p<0.05). Inflammatory biomarkers at 6-month and 12-month follow-ups, a statistically significant difference in the level of biomarkers (IL-1β and TNF-α) were observed in both groups from baseline (p<0.05). However, at baseline, no significant difference was noted in both groups (p> 0.05). The microbiological assessment showed a significant drop in the bacterial count in participants treated with both regimes i.e., SPT alone and PDT adjunct to SPT. ConclusionPhotodynamic therapy (PDT) adjunct to surgical periodontal treatment (SPT) in severe periodontitis improves microbiological and periodontal parameters and lowers the level of proinflammatory cytokines.

Periodontal, Microbiological, and Proinflammatory Cytokines Levels in Fixed Orthodontics Patients Treated with Photodynamic Therapy Adjunct to Full Mouth Scaling.

Objective: To assess the efficiency of photodynamic therapy (PDT) adjunct to full mouth scaling (FMS) in improving periodontal, microbiological, and proinflammatory cytokines levels in patients undergoing fixed orthodontics treatment (FOT). Materials and methods: The study recruited 60 teenage patients who were undergoing FOT. All the patients were arbitrarily divided into two groups: Group 1, FMS +PDT and Group 2, FMS alone. Plaque scores (PS), bleeding on probing (BOP), and probing depth (PD) were assessed. Levels of biomarkers interleukin (IL)-6 and tumor necrosis factor-alpha (TNF-α) using enzyme-linked immunosorbent assay were evaluated. Samples were collected from gingival plaque for estimation of Prevotella intermedia and Porphyromonas gingivalis load. All measurements were completed at three intervals baseline, 4th week, and 8th week. Post Hoc corrections and intergroup comparisons were examined using Student's t-test and Bonferroni correction. To find differences between repeated follow-ups, analysis of variance (ANOVA) multiple rank tests were used. Results: At baseline, all the gingival parameters displayed comparable outcomes between patients of Group 1 and Group 2 (p > 0.05). However, at 4 and 8 weeks of follow-up, PS and BOP among tested groups exhibited significantly lower values than baseline (p < 0.001). At 8 weeks, there was a significant difference in PS between the two groups tested. Moreover, at 4 and 8 weeks, BOP revealed a significant difference between the groups. PD remains comparable with baseline at follow-up visits (p > 0.05). A significant decrease in IL-6 and TNF-α levels was observed in both investigated groups at 4 and 8 weeks of baseline. Moreover, it was identified that P. intermedia and P. gingivalis were reduced significantly at 4 weeks. Moreover, a significant difference existed between both Group 1 and Group 2 at 4 and 8 weeks of recall visit (p < 0.05). Conclusions: The use of photodynamic treatment adjuvant to FMS aids in improving periodontal parameters and cytokines levels.

Notes on the declining American institutions in university rankings: a trend against the Germany–Britain–America shift?

Previous research has outlined the ongoing decline of British universities, just as the decline of German universities before it. By revisiting the performances of the ‘top’ universities worldwide in multiple rankings and grading systems, this study outlines an ongoing decline of American universities in university rankings. The falls of the USA’s public universities are especially notable. Normally, scholars believe the shift of the world’s scientific center is from the European continent to Britain, and then to America. However, the performance of universities worldwide in recent decades just showcases a different picture, in which American universities are declining at a faster speed than those in Britain and the European continent.

RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

With the rapid development of the internet of things (IoT), electricity consumption data can be captured and recorded in the IoT cloud center. This provides a credible data source for enterprise credit scoring, which is one of the most vital elements during the financial decision-making process. Accordingly, this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data. Instead of predicting the credit rating, our method can generate an absolute credit score by a novel deep ranking model–ranking extreme gradient boosting net (rankXGB). To boost the performance, the rankXGB model combines several weak ranking models into a strong model. Due to the high computational cost and the vast amounts of data, we design an edge computing framework to reduce the latency of enterprise credit evaluation. Specially, we design a two-stage deep learning task architecture, including a cloud-based weak credit ranking and an edge-based credit score calculation. In the first stage, we send the electricity consumption data of the evaluated enterprise to the computing cloud server, where multiple weak-ranking networks are executed in parallel to produce multiple weak-ranking results. In the second stage, the edge device fuses multiple ranking results generated in the cloud server to produce a more reliable ranking result, which is used to calculate an absolute credit score by score normalization. The experiments demonstrate that our method can achieve accurate enterprise credit evaluation quickly.

Weight set decomposition for weighted rank and rating aggregation: An interpretable and visual decision support tool

The problem of interpreting or aggregating multiple rankings is common to many real-world applications. Perhaps the simplest and most common approach is a weighted rank aggregation, wherein a (convex) weight is applied to each input ranking and then ordered. This paper describes a new tool for visualizing and displaying ranking information for the weighted rank aggregation method. Traditionally, the aim of rank aggregation is to summarize the information from the input rankings and provide one final ranking that hopefully represents a more accurate or truthful result than any one input ranking. While such an aggregated ranking is, and clearly has been, useful to many applications, it also obscures information. In this paper, we show the wealth of information that is available for the weighted rank aggregation problem due to its structure. We apply weight set decomposition to the set of convex multipliers, study the properties useful for understanding this decomposition, and visualize the indifference regions. This methodology reveals information–that is otherwise collapsed by the aggregated ranking–into a useful, interpretable, and intuitive decision support tool. Included are multiple illustrative examples, along with heuristic and exact algorithms for computing the weight set decomposition.

A hybrid approach to targeting social assistance

Proxy means testing (PMT) and community-based targeting (CBT) are two of the leading methods for targeting social assistance in developing countries. We present a hybrid targeting method that incorporates CBT’s emphasis on local information and preferences with PMT’s reliance on verifiable indicators. Specifically, we outline a Bayesian framework for targeting that resembles PMT in that beneficiary selection is based on a weighted sum of sociodemographic characteristics, but we instead propose calibrating the weights to preference rankings from community targeting exercises. We discuss several practical extensions to the model, including a generalization to multiple rankings per community, an adjustment for elite capture, a method for incorporating auxiliary information on potential beneficiaries, and a dynamic updating procedure. We further provide an empirical illustration using data from Burkina Faso and Indonesia, which shows that our method achieves error rates lower than what PMT achieves when targeting household expenditures.