Subjective Ranking Research Articles

Cannabis Uses in Dentistry: A Bibliometric Analysis.

Background: This bibliometric review seeks to understand metrics of papers, authors, journals, and universities, about the benefits of the therapeutic application of Cannabis sativa (CS), as well as the most harmful effects associated to its use. Methods: The main search strategy applied to the topic was conducted in Web Of Science Core Collection on February 2024. A crossmatch of the number of citations was performed in Scopus and Google Scholar. The analyses were carried out in VOSviewer and Altmetric for PubMed and Google Scholar. Results: Of a total of 196 records, 53 articles were included for analysis. There were 25 publications on either therapeutic or harmful effects. In the ranking of subjects, those of greatest interest were general oral health and periodontics, with 53% of the total. The most cited paper was authored by Thomson et al. (2008) with 85 citations, allowing the University of Otago to be the most cited. Although JAMA was the most cited journal, in the dental field this corresponded to the Journal of Clinical Periodontology. In relation to the distribution by country, the United States received the largest number of citations and New Zealand second. Related to dentistry, in the cluster analysis, keywords more occurrent were "periodontal disease" and "periodontitis". Conclusions: In the past 4 years, there has been a superlative growth in CS papers related to oral health effects. This growth follows the social and political events related to CS legalization in some countries and reveals that the use of CS in dentistry is an emerging research field.

Ways of Referential Ranking of Subjects in Russian Proverbs with Forms of the Second Person Singular

Ways of Referential Ranking of Subjects in Russian Proverbs with Forms of the Second Person Singular

Striving for global excellence in research: challenges in world rankings for Pakistani medical Universities

<p>University of Health Sciences (UHS), Lahore stands as the sole representative from Punjab province alongside Sindh (Agha Khan Medical University, and Dow University of Health Sciences Karachi) to secure its position among the top 800 medical universities of the world in 2024 QS World Subject Rankings. The absence of most Pakistani medical universities from these rankings underscores a broader issue within the country's higher education system. Factors contributing to this include insufficient research output, lack of international collaboration, inadequate funding, and limited focus on innovation and academic reputation. To address this issue, the research and development (R&D) in medical sciences require substantial investment, which is currently lacking especially within the public sector medical universities. Without significant funding, universities cannot support high-quality research projects or attract top-tier faculty. Secondly, there is a dire need for enhancing the quality and quantity of research publications. Pakistani universities often lag in producing high-impact research papers, a crucial criterion for international rankings. Thirdly, international collaboration and partnerships are limited, reducing the global visibility and impact of Pakistani research. Furthermore, improving the overall academic environment by attracting and retaining high-caliber faculty, modernizing curricula, and fostering a culture of alumni exchange and engagements will contribute to better rankings.</p>

Subclinical impairment of left ventricular myocardium function in type 2 diabetes mellitus patients with or without hypertension

BACKGROUND Cardiovascular disease has been the leading cause of morbidity and mortality for type 2 diabetes mellitus (T2DM) patients over the last decade. AIM To determine whether layer-specific global longitudinal strain (GLS) combined with peak strain dispersion (PSD) can be used to assess left ventricle (LV) myocardium systolic dysfunction in T2DM patients or without hypertension (HP). METHODS We enrolled 97 T2DM patients, 70 T2DM + HP patients and 101 healthy subjects. Layer-specific GLS and PSD were calculated by EchoPAC software in apical three-, four- and two-chamber views. GLS of the epimyocardial, middle-layer and endomyocardial (GLSepi, GLSmid, and GLSendo) were measured and recorded. Receiver operating characteristic analysis was performed to detect LV myocardium systolic dysfunction in T2DM patients. RESULTS There were significant differences in GLSepi, GLSmid, GLSendo, and PSD between healthy subjects, T2DM patients and T2DM patients with HP (P < 0.001). Trend tests yielded the ranking of healthy subjects > T2DM patients > T2DM with HP patients in the absolute values of GLSepi, GLSmid and GLSendo (P < 0.001), while PSD was ranked healthy subjects < T2DM < T2DM with HP (P < 0.001). Layer-specific GLS and PSD had high diagnostic efficiency for detecting LV myocardium systolic dysfunction in T2DM patients, however, the area under the curve (AUC) for layer-specific GLS and PSD combined was significantly higher than the AUCs for the individual indices (P < 0.05). CONCLUSION Layer-specific GLS and PSD were associated with LV myocardium systolic dysfunction in T2DM patients, T2DM patients with HP. T2DM patients with HP have more severe LV myocardium systolic dysfunction than T2DM patients without HP and normal control patients. The combination of layer-specific GLS and PSD may provide additional prognostic information for T2DM patients with or without HP.

Training deep learning based dynamic MR image reconstruction using open-source natural videos

To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Learning was performed for a range of DL architectures (VarNet, 3D UNet, FastDVDNet) and corresponding sampling patterns (Cartesian, radial, spiral) either from true multi-coil cardiac MR data (N = 692) or from synthetic MR data simulated from Inter4K natural videos (N = 588). Real-time undersampled dynamic MR images were reconstructed using DL networks trained with cardiac data and natural videos, and compressed sensing (CS). Differences were assessed in simulations (N = 104 datasets) in terms of MSE, PSNR, and SSIM and prospectively for cardiac cine (short axis, four chambers, N = 20) and speech cine (N = 10) data in terms of subjective image quality ranking, SNR and Edge sharpness. Friedman Chi Square tests with post-hoc Nemenyi analysis were performed to assess statistical significance. In simulated data, DL networks trained with cardiac data outperformed DL networks trained with natural videos, both of which outperformed CS (p < 0.05). However, in prospective experiments DL reconstructions using both training datasets were ranked similarly (and higher than CS) and presented no statistical differences in SNR and Edge Sharpness for most conditions.The developed pipeline enabled learning dynamic MR reconstruction from natural videos preserving DL reconstruction advantages such as high quality fast and ultra-fast reconstructions while overcoming some limitations (data scarcity or sharing). The natural video dataset, code and pre-trained networks are made readily available on github.

Simplified Spectrum Score (S3) app for pathogen-agnostic antimicrobial drug spectrum ranking to assess for antimicrobial de-escalation events

Antimicrobial/antibiotic de-escalation (ADE) is a key feature of antimicrobial stewardship programs (ASP) that relies mainly on individual panels for determining ADE events based on subjective ranking of antibiotics’ spectrum activity. The lack of consensus among ASP experts leads to reproducibility issues in the measure of this clinical outcome, making difficult to assess its real impact on patient care. The S3 score (Simplified Spectrum Score) app was developed to allow an objective ranking of antibiotics. Ranking was achieved by developing a database harboring pairs of bacteria-antibiotics for which each molecule was assigned a score based on published and clinically validated data from a recognized international committee. S3 score shows a strong correlation relationship and substantial agreement to a clinically validated spectrum score, and its framework enables any person to use it for ADE detection without assuming prior knowledge or training. In addition, its design enables regular updates and sustainability.

Variability of Uncertainty in Mental Reconstruction of Danger and Safety

Subjective images of danger and security depend on the context of uncertainty. This article introduces a mental reconstruction of danger and safety for different types of uncertainty. The authors used the methods of associative experiment, subjective ranking, and semantic differential. The empirical data were processed by averaging, ranking, factor analysis, and the Mann-Whitney criterion. The survey involved students from several Russian metropolitan and provincial universities (19–20 y.o.; n = 105; 74 females). Danger demonstrated a less complex mental structure than safety. The mental reconstruction of danger / safety included three types of uncertainty. Temporal uncertainty included period, time, and frequency of danger / safety. Objective uncertainty was connected with the place, environment, and source of danger / safety. Subjective uncertainty covered controllability, scale, and cause of danger / safety. Uncertainty proved more important for danger than for safety. The subjective level of danger grew together with objective uncertainty, especially when the source of danger was unclear, and went down with temporal uncertainty. The subjective level of security increased with temporal and subjective uncertainty and decreased with objective uncertainty. When the subjective significance of danger and safety went up, the differentiation of uncertainty in their assessment became less significant. The study identified the effect of variability of uncertainty on the mental reconstruction made by the subjects of danger / security, as well as identified some understudied areas.

Rapid 2D 23Na MRI of the calf using a denoising convolutional neural network

Purpose23Na MRI can be used to quantify in-vivo tissue sodium concentration (TSC), but the inherently low 23Na signal leads to long scan times and/or noisy or low-resolution images. Reconstruction algorithms such as compressed sensing (CS) have been proposed to mitigate low signal-to-noise ratio (SNR); although, these can result in unnatural images, suboptimal denoising and long processing times. Recently, machine learning has been increasingly used to denoise 1H MRI acquisitions; however, this approach typically requires large volumes of high-quality training data, which is not readily available for 23Na MRI. Here, we propose using 1H data to train a denoising convolutional neural network (CNN), which we subsequently demonstrate on prospective 23Na images of the calf. Methods1893 1H fat-saturated transverse slices of the knee from the open-source fastMRI dataset were used to train denoising CNNs for different levels of noise. Synthetic low SNR images were generated by adding gaussian noise to the high-quality 1H k-space data before reconstruction to create paired training data. For prospective testing, 23Na images of the calf were acquired in 10 healthy volunteers with a total of 150 averages over ten minutes, which were used as a reference throughout the study. From this data, images with fewer averages were retrospectively reconstructed using a non-uniform fast Fourier transform (NUFFT) as well as CS, with the NUFFT images subsequently denoised using the trained CNN. ResultsCNNs were successfully applied to 23Na images reconstructed with 50, 40 and 30 averages. Muscle and skin apparent TSC quantification from CNN-denoised images were equivalent to those from CS images, with <0.9 mM bias compared to reference values. Estimated SNR was significantly higher in CNN-denoised images compared to NUFFT, CS and reference images. Quantitative edge sharpness was equivalent for all images. For subjective image quality ranking, CNN-denoised images ranked equally best with reference images and significantly better than NUFFT and CS images. ConclusionDenoising CNNs trained on 1H data can be successfully applied to 23Na images of the calf; thus, allowing scan time to be reduced from ten minutes to two minutes with little impact on image quality or apparent TSC quantification accuracy.

Fostering academic resilience in higher education

Introduction: Students who develop resilience are more likely to sustain high levels of achievement, motivation, and performance despite the presence of stressful conditions.&#x0D; Method: Sixty-eight collegiate students completed pre- and post-Academic Resilience Scale-30 assessments before and after a brief, virtual educational program on common resilience strategies. Subjects then ranked the strategies in the educational program as “most helpful” and “least helpful.”&#x0D; Results: Data analysis revealed that there was a statistically significant difference between pre- and post- Academic Resilience Scale-30 scores, suggesting that the educational program improved resilience.&#x0D; Discussion: Subject ranking of strategies further revealed that the most helpful strategies were mantras and cost/benefit analysis while the least helpful strategies were mantras, writing down feelings, and cost-benefit analysis.&#x0D; Conclusion: Educational resilience programs have the potential to improve resilience amongst students enrolled in higher education. However, due to the conflicting opinions found in the rankings of strategies, future studies and resilience programs should focus on providing a variety of strategies to best support a diverse student population.

Strategies for Activating Independent Learning Activities of Students in Biology Classes

The article is dedicated to methods of activating students' independent learning activities during biology classes. It is proved that independent work in education holds significant importance for both the learning process and students of general secondary education. As noted by the authors of the article, the successful implementation of such activities depends on the teacher's ability to effectively manage them. Among the innovative methods there are: problem-based learning, projects and research, group work, utilization of internet resources and multimedia, task solving, and independent preparation of presentations or reports. By following these principles, it is possible to contribute to the development of students with their own worldview and a steadfast life position. The article highlights the importance of active independent learning activities during lessons, particularly in biology, and provides lesson plans developed using methods aimed at fostering such activities among seventh-grade students. Additionally, the results of student surveys are presented, demonstrating the level of interest in biological education, ranking of subjects in personal significance, level of students’ independent activity, and their suggestions for diversifying and improving the process of biology learning. It was revealed that 13% of students exhibit a high level of independent learning activity, 54% demonstrate an average level, while 32% have a low level of independent learning activity. It is noted that activation of students' independent learning activities in the process of studying biology is crucial for both their personal growth and overall development. As biology has its peculiarities, students' independent activity becomes the foundation for their success and the development of scientific thinking. Taking into account these aspects, it can be argued that students' independent cognitive activity in the study of biology is essential for their comprehensive development and the formation of scientific thinking and skills useful in future life and professional activities. According to the research, to achieve such results, teachers should create conditions conducive to the development of students' independence and cognitive abilities, providing them with the opportunity to independently explore, study, and conceptualize new knowledge.

Losing objectivity: The questionable use of surveys in the Global Ranking of Academic Subjects

Abstract The Academic Ranking of World Universities (ARWU) is one of the most well-known university rankings, recognized for its objective and reproducible methodology. In contrast, the Global Ranking of Academic Subjects (GRAS), which ranks institutions by scientific subjects and is also elaborated by Shanghai Ranking Consultancy (SRC), introduces methodological differences that deviate from the ARWU’s objectivity. This is due to the use of SRC’s Academic Excellence Survey to define two of the GRAS’s five indicators. Specifically, the Top indicator counts publications in journals determined by respondents as top tier in their field, and the Award indicator does the same for prizes. An examination of this survey suggests the presence of potential biases, especially in participant selection and journal identification, among which an Anglo-Saxon bias is prominently evident. Likewise, there is a potential risk that the selection of journals in some cases may be influenced, potentially masking conflicts of interest, such as involvement in editorial committees that could sway this selection. As a result, relying on surveys instead of adhering to established bibliometric standards can lead to inconsistencies and subjectivity, especially if not rigorously conducted. Such methodologies pose a risk to the trustworthiness of tools crucial for university policymaking.

The Small Pixel Effect in Ultra-High-Resolution Photon-Counting CT of the Lumbar Spine.

Image acquisition in ultra-high-resolution (UHR) scan mode does not impose a dose penalty in photon-counting CT (PCCT). This study aims to investigate the dose saving potential of using UHR instead of standard-resolution PCCT for lumbar spine imaging. Eight cadaveric specimens were examined with 7 dose levels (5-35 mGy) each in UHR (120 × 0.2 mm) and standard-resolution acquisition mode (144 × 0.4 mm) on a first-generation PCCT scanner. The UHR images were reconstructed with 3 dedicated bone kernels (Br68 [spatial frequency at 10% of the modulation transfer function 14.5 line pairs/cm], Br76 [21.0], and Br84 [27.9]), standard-resolution images with Br68 and Br76. Using automatic segmentation, contrast-to-noise ratios (CNRs) were established for lumbar vertebrae and psoas muscle tissue. In addition, image quality was assessed subjectively by 19 independent readers (15 radiologists, 4 surgeons) using a browser-based forced choice comparison tool totaling 16,974 performed pairwise tests. Pearson's correlation coefficient ( r ) was used to analyze the relationship between CNR and subjective image quality rankings, and Kendall W was calculated to assess interrater agreement. Irrespective of radiation exposure level, CNR was higher in UHR datasets than in standard-resolution images postprocessed with the same reconstruction parameters. The use of sharper convolution kernels entailed lower CNR but higher subjective image quality depending on radiation dose. Subjective assessment revealed high interrater agreement ( W = 0.86; P < 0.001) with UHR images being preferred by readers in the majority of comparisons on each dose level. Substantial correlation was ascertained between CNR and the subjective image quality ranking (all r 's ≥ 0.95; P < 0.001). In PCCT of the lumbar spine, UHR mode's smaller pixel size facilitates a considerable CNR increase over standard-resolution imaging, which can either be used for dose reduction or higher spatial resolution depending on the selected convolution kernel.

Graph model for conflict resolution based on the combination of probabilistic uncertain linguistic and EDAS method

The ranking of decision makers (DMs)’ preferences for feasible states in the graph model for conflict resolution (GMCR) is crucial for accurately determining stability results. This paper addresses the issue of subjective ranking methods lacking theoretical foundation and causing ambiguity when the number of feasible states is high by proposing the implementation of the multi-attribute decision-making (MADM) method in the GMCR. The paper utilizes the average level to choose evaluation based on distance from average solution (EDAS) method for determining the DM’s preference ranking, which can effectively reduce the impact of anomalous evaluations. Further, the PUL-EDAS method based on probabilistic uncertainty linguistics (PUL) is developed, which overcomes the shortcomings of the traditional EDAS method, which only applies to the simple evaluation of information. The PUL aligns with DMs’ daily evaluation practice by providing an interval for the quality of qualitative linguistic evaluations. Furthermore, it utilizes an objective aggregation method to calculate comprehensive evaluation information from all DMs. In addition, the four fundamental stability definitions, applicable solely under crisp preferences, are extended to the PUL context, providing related extended definitions. Finally, to ensure the scientific validity and practicality of the proposed theory, this paper selects digital rural governance as the research context for conflict calculus analysis, comparing it with other MADM methods in the preference ranking section.

A Visual Cortex-Attentive Deep Convolutional Neural Network for Digital Image Design

With the proliferation of advanced visualization techniques in visual communication, enhancing digital image quality remains a persistent challenge. This study presents a sophisticated Convolutional Neural Network (CNN) model to optimize image processing. The model incorporates a multi-stage architecture attentive to biological visual pathways. Inter-subnetwork connections enable integrated feature learning, guided by adaptive weighting of luminance, color, orientation, and edge maps. Spatial and channel attention modules further enrich feature interplay. When evaluated on the LIVE 3D Phase dataset, the approach demonstrates marked improvements, with saliency maps closely mirroring human visual perception. Pearson Correlation Coefficient and Histogram Intersection metrics exceed conventional models, at 0.6486 and 0.7074 respectively. Testing across distortion types reveals strong agreement with subjective rankings, confirming the model's effectiveness. By combining automated feature extraction with insights from visual cortex mechanisms, this bio-inspired CNN framework significantly enhances image optimization and quality. The scalable approach provides a foundation for next-generation computer vision and machine learning applications.

Clinical trainee performance on task-based AR/VR-guided surgical simulation is correlated with their 3D image spatial reasoning scores.

This paper describes a methodology for the assessment of training simulator-based computer-assisted intervention skills on an AR/VR-guided procedure making use of CT axial slice views for a neurosurgical procedure: external ventricular drain (EVD) placement. The task requires that trainees scroll through a stack of axial slices and form a mental representation of the anatomical structures in order to subsequently target the ventricles to insert an EVD. The process of observing the 2D CT image slices in order to build a mental representation of the 3D anatomical structures is the skill being taught, along with the cognitive control of the subsequent targeting, by planned motor actions, of the EVD tip to the ventricular system to drain cerebrospinal fluid (CSF). Convergence is established towards the validity of this assessment methodology by examining two objective measures of spatial reasoning, along with one subjective expert ranking methodology, and comparing these to AR/VR guidance. These measures have two components: the speed and accuracy of the targeting, which are used to derive the performance metric. Results of these correlations are presented for a population of PGY1 residents attending the Canadian Neurosurgical "Rookie Bootcamp" in2019.

РАЗВИТИЕ СЕТИ АКАДЕМИЧЕСКИХ ПАРТНЕРОВ КАК ФАКТОР ПОВЫШЕНИЯ КАЧЕСТВА ПОДГОТОВКИ В ВУЗЕ

One of the principal priorities in higher education development strategy is improving the quality of training at university. In this study employment is considered a key indicator of university training quality. The study included five universities in Russia and Belarus over the period from February to April 2023; a total of 436 respondents were covered. It was determined that the level of importance assigned to employment in terms of training quality may vary depending on the subject category within the university ecosystem. A direct correlation was established between the ranking of key subjects of the university ecosystem and the evaluation of their impact on employment as an indicator of training quality. The mechanisms for building an academic partner network are described, based on the network theory (T. Rowley), the stakeholder approach (R. Freeman), as well as the ecosystem approach to managing the training quality at a university. From the perspective of management practice, it is significant that the study results confirmed the author’s thesis: increasing the level of employment through building a partner network can improve university quality.

Lower social class, better social skills? A registered report testing diverging predictions from the rank and cultural approaches to social class

Are people with lower socioeconomic status (SES) better than those with higher SES at empathic accuracy, or recognizing others' thoughts and feelings? Two psychological approaches to the study of SES say they are, but emphasize different reasons. The rank approach argues that because individuals with lower SES experience low rank, they feel less in control and more threatened by others, so it is more valuable for them to understand others' mental states. The cultural approach argues that because lower SES cultures foster more interdependent values, people steeped in those cultures focus more on others, leading them to better understand others' mental states. Previous tests of the basic hypothesis these two approaches share have yielded mixed evidence. This registered report uses a large-scale, nationally representative sample to accomplish two goals. First, it tests the basic shared hypothesis, finding that lower subjective rank, social class cultural group, and income—but not education—all predict better empathic accuracy. Second, it explores additional research questions, finding that subjective rank more strongly predicts empathic accuracy compared to SES cultural group (consistent with the rank approach), but childhood SES more strongly predicts empathic accuracy than adulthood SES (consistent with the cultural approach). Results regarding the moderating role of the valence of the mental state being recognized were not consistent. We conclude there is indeed a negative association between social class and empathic accuracy, and discuss the degree to which this is due to the psychology of rank and childhood cultural socialization.

Measuring woke culture in universities: a diagnostic approach

ObjectivesThis paper aims to present a synthetic diagnostic tool for assessing wokeism in universities, offering a comprehensive framework derived from an extensive literature review. The purpose is to measure and evaluate the level and impact of wokeism, identifying strengths and weaknesses within university contexts. The framework development involves defining dimensions and indicators of wokeism, collecting and analyzing data through diverse sources like surveys and interviews, scoring and ranking wokeism levels.Material and methodsThis paper presents a synthetic diagnostic tool designed for the assessment of wokeism in universities. It extensively reviews literature on wokeism, offering a framework for the creation of this diagnostic tool. The provided examples demonstrate practical applications for measuring and evaluating the level and impact of wokeism within university settings.ResultsThe paper highlights several limitations and implications that warrant consideration. First, the broad and general definition of wokeism employed might overlook nuanced variations within the movement. Second, reliance on secondary sources and hypothetical data raises concerns about the representation of the actual situation of wokeism in universities. Third, the subjective scoring and ranking system employed may lack the validity and reliability required for a comprehensive assessment.ConclusionsThe paper proposes directions for future research and practice. It recommends refining and operationalizing the definition and measurement of wokeism, testing and validating the tool and framework with primary and empirical data, and applying more objective and rigorous methods for scoring and ranking.

A human-machine collaborative approach measures economic development using satellite imagery

Machine learning approaches using satellite imagery are providing accessible ways to infer socioeconomic measures without visiting a region. However, many algorithms require integration of ground-truth data, while regional data are scarce or even absent in many countries. Here we present our human-machine collaborative model which predicts grid-level economic development using publicly available satellite imagery and lightweight subjective ranking annotation without any ground data. We applied the model to North Korea and produced fine-grained predictions of economic development for the nation where data is not readily available. Our model suggests substantial development in the country’s capital and areas with state-led development projects in recent years. We showed the broad applicability of our model by examining five of the least developed countries in Asia, covering 400,000 grids. Our method can both yield highly granular economic information on hard-to-visit and low-resource regions and can potentially guide sustainable development programs.

Soft Wearable Haptic Display and Flexible 3D Force Sensor for Teleoperated Surgical Systems

AbstractHaptic (touch) is important for effective interaction with the surroundings. In teleoperated surgical systems, its absence leads to reduced perception, making delicate tasks difficult and resulting in unexpected damage to surrounding tissues. To enhance safety, a force‐feedback system and haptic device are needed. However, existing haptic prototypes are associated with rigid, bulky, and heavy components, making effective communication with the skin problematic. This paper presents a teleoperated endoscopic surgical system with an integrated 3D force sensor and real‐time haptic feedback. A surgical robotic arm is remotely controlled by a soft haptic glove incorporated 3‐axis cutaneous device and a finger kinesthetic module. The 3D force sensor is constructed from hydraulic filament soft sensors that can induce pressure change under strain. To enable precise motion, the haptic glove is operated by a feedforward controller and a master‐slave architecture. Experiments with human subjects (n = 15) show that cutaneous and kinesthetic feedback significantly improves the user's performance (9.4 out of 10) compared to no haptic feedback (2.27 out of 10). Finally, subjects rank the new system as highly wearable, comfortable, and effective, which is expected to bridge a gap in the surgical field and support the future development of advanced teleoperated systems.