Comparison Of Approaches Research Articles

Semi-supervised learning for skeleton behavior recognition: A multi-dimensional graph comparison approach

Semi-supervised learning for skeleton behavior recognition: A multi-dimensional graph comparison approach

Formulation and evaluation of herbal tablet of jackfruit root for the treatment of antiasthmatic activity

Abstract Jackfruit is a highly nutritious plant. The ancient fruit known as jackfruit (Artocarpus heterophyllus Lam) is commonly eaten raw. Documenting the therapeutic value of the main jackfruit parts (Artocarpus heterophyllus L.) was the primary goal of this study. It contains more protein, calcium, iron, and Thiamine. Jackfruit contains many classes of phytochemicals such as carotenoids, flavonoids, volatile acids sterols, and tannins, with varying concentrations. The excellent medicinal efficacy of Artocarpus has long been recognized. Phenolic compound test. Next, using a direct comparison approach, we developed pills for asthma. After that, check evaluations such as degradability, hardness, friability, homogeneity of content, weight change, solubility, etc. The health benefits of jackfruit are attributed to its many physicochemical applications. Jackfruit is some tropical fruit rich in 4,444 nutrients such as carbohydrates, proteins, vitamins, minerals, and digestive fiber to maintain bronchial pain relief. In the case of asthma, a person suffers from bronchospasm and this herbal tablet acts as a bronchodilator. Therefore, we examined the antiasthamatic characteristics using a herbal mixture prepared from jackfruit root. Jackfruit contains vitamin A, vitamin C, thiamin, riboflavin, calcium, potassium, iron, sodium, zinc, and niacin, among many other nutrients.

JOINT LOW-RANK REPRESENTATION AND TRANSFER MULTI-VIEW LEARNING APPROACH FOR EEG EPILEPSY CLASSIFICATION

Classification of epileptic electroencephalograms can provide strong technical support for early warning and monitoring of epilepsy. However, Electroencephalogram (EEG) has characteristics of specificity, multi-feature, and susceptibility to noise interference. Therefore, this paper proposes a joint low-rank representation and transfer multi-view learning (LRR-TML) approach for EEG epilepsy classification. The LRR-TML approach inherits the advantages of low-rank representation, filters redundant features and noise information, and focuses on the main feature and significant feature of EEG. The LRR-TML approach introduces the ideas of transfer learning and multi-view learning on the framework of low-rank representation, mines the linear correlation between different domains in the subspace through low-rank representation, and effectively transfers the discriminative knowledge of the source domain to the target domain. According to the principles of complementarity and consistency of views, the LRR-TML approach comprehensively describes EEG by integrating various features, maximizing the consistency between different views and minimizing the divergence between views. Experimental results on the Bonn dataset show that the LRR-TML approach achieves the best classification results among all comparison approaches.

A new way to address missing data in late-stage clinical trials

According to ICH E9(R1), defining the estimand comes before defining the analysis approach, and the strategies for addressing intercurrent events are key components of the estimand. With the composite strategy, the problem of missing data disappears, because it becomes part of the endpoint definition. It is this perspective that we adopt in addressing the problem of missing data. We propose comparing patients in a pairwise manner to determine which patient fared better, one patient from each group, taking into account the reason for and timing of missingness. For purposes of illustration, reasons for missingness are placed into three categories: (1) death or adverse events, (2) administration of rescue medication (treated as missing even if patient continues in the study, or a poor score is assigned), and (3) other reasons such as loss to follow-up or withdrawal of consent. Each pair of patients is compared on the endpoint of interest. The comparison outcomes are determined based on the presence of missing data, its category, and timing. For instance, if both patients in a pair have received rescue medication, the patient with the later time of rescue medication is considered to have fared better. The overall treatment effect is estimated from combining results across all pairwise comparisons. This method allows the reason and timing of missing data to contribute to the assessment of treatment effects, providing a solution to some limitations of existing methods. Thus, the pairwise comparison approach addresses the missing data problem transparently via the composite strategy.

The Impact of Digital Transformation on Service Models and Employee Skill Requirements in Taiwan's Real Estate Brokerage Industry

This paper investigates how digital transformation impacts service delivery models and employee skill requirements in Taiwan's real estate brokerage industry. Through a multiple-case comparison approach, data was collected from 200 participants across real estate firms to examine organizational adaptation to technological change. The research employed structural equation modeling and hierarchical linear modeling to analyze relationships between compensation satisfaction (β = 0.45, p < 0.001), digital competency levels, and internal case circulation patterns. Results demonstrate that digital technologies significantly reshape brokerage operations, with digitalized companies achieving 32% higher client acquisition rates and 45% reduction in transaction processing time. Analysis reveals that employee digital literacy accounts for 28% of performance variance, while organizational support systems explain 23% of technology adoption rates. The findings extend service innovation literature by establishing empirical links between compensation systems and digital transformation success, providing a practical framework for technology integration in Taiwan's real estate sector. This research offers evidence-based recommendations for firms implementing digitalization strategies and developing employee capabilities in response to industry evolution.

Linguistic landscape in russian-speaking schools of Visaginas

The article describes the sociolinguistic situation in Visaginas, while also introducing the main theoretical provisions and terms of sociolinguistics (sociolinguistic situation, language policy, linguistic landscape, enculturation, etc.). The analytical part examines specific data – cultural signs compiled in the Russian-speaking schools in Visaginas – allowing the linguistic landscape to be examined through the lens of enculturation. The linguistic landscape analysis and the comparison approach were applied within the course of work. 1,142 units ‒ symbols of Russian and Lithuanian cultures compiled in three educational institutions with the Russian language of tuition – were analyzed. The material that had been classified in accordance with the groups (visual, complex, textual signs) allowed us to establish the predominance of the Lithuanian culture in the educational institutions of Visaginas Town. It also showed the presence of the signs of culture of different types.The importance of this research stems from the understudied language landscape, particularly in educational institutions. Furthermore, it should be noted that the educational linguistic environment of Visaginas has never been studied before.

Assessing Cognitive and Perceptual Abilities in Imaginative and Mainstream Elementary Schools: A Case Comparison

This study investigates the impact of imaginative pedagogy on the cognitive and perceptual abilities of elementary school students aged 7-8, using a case comparison approach. Focusing on a Waldorf school as a case for imaginative education and a mainstream Greek school, the research examines abilities including Spatial, Linguistic, Kinesthetic, Naturalistic, Mathematical and Musical. The study utilized a mixed method consisting of playful assessment activities and classroom observations to evaluate these abilities comprehensively. The findings reveal that students in the Waldorf school demonstrate significant advantages in the Spatial, Linguistic, Kinesthetic and Naturalistic areas, attributed to the school's imaginative teaching methods. More specifically, imaginative pedagogy is shown to resonate well with competences related to physical activities, language processing, understanding of the natural world, and visual-spatial reasoning. In contrast, there are no significant differences in Math-Logic and Musical abilities between the two school types, despite the traditional emphasis on language and mathematics in mainstream Greek schools. The study highlights the potential benefits of imaginative pedagogy for holistic cognitive development, suggesting its applicability in mainstream education.

A novel intelligent bearing fault diagnosis method based on image enhancement and improved convolutional neural network

Bearing fault diagnostic is crucial for ensuring the normal operation of equipment. However, the working environment of mechanical equipment is often very harsh, the collection of fault data is limited in the actual industrial process. The problem of insufficient fault samples occurs, which makes the feature extraction ability of the deep learning models drop dramatically. To solve this terrible problem, an intelligent diagnosis framework based on image enhancement and an improved convolutional neural network (IEICNN) is outlined in this article. First, a signal conversion method based on feature fusion is employed to convert multi-channel one-dimensional vibration signals into red − green − blue (RGB) images to obtain more comprehensive fault data representation. In addition, a new image enhancement method is proposed, which uses the improved Squeeze and Excitation-Residual Network (SE-ResNet) to learn and extract the advanced feature map, and uses deconvolution neural networks to reconstruct RGB image. Finally, the Softmax classifier is applied to identify the health conditions of the bearing. The superiority and robustness of IEICNN are validated using two bearing datasets. By comparing with several progressive comparison approaches, it is proven that the proposed approach has better robustness and effectiveness in the case of limited data scarcity.

Health technology assessment of nailfold capillaroscopy and digital retina imaging in diabetes screening

AimThis study aims to identify the criteria for the evaluation of medical technologies in diabetes screening and then perform health technology assessment (HTA) for two technologies: digital retina imaging (DRI) and Nailfold Capillaroscopy (NFC). MethodsA multicriteria decision-making tool measuring attractiveness using a categorical-based evaluation technique (MACBETH) was used to calculate and compare alternatives. A focus group of ten decision-makers with more than ten years of experience in health technology was used for the decision-making. The Delphi Method was used to get a consensus about comparing criteria and alternatives to these criteria. The sampling method used in the study was the nonprobability purposive sampling method. ResultsThe study concluded that clinical efficacy (0.42) is the most important criterion for evaluating medical technologies, followed by cost-effectiveness (0.27). At the same time, ethical considerations were found to be less important (0.03). The sensitivity analysis concluded that the decision is sensitive to clinical efficacy and cost-effectiveness changes. The study found that none of the alternatives dominates others as an option for early detection of diabetes through vascular changes. LimitationsThis study primarily uses systemic literature review methods to identify criteria for evaluating alternatives. There may be additional criteria for evaluation. ConclusionThis study provides an approach for a multicriteria comparison of medical devices. The study findings are useful for public health professionals and health policymakers. Plain language summaryThis study assesses nailfold capillaroscopy (NFC) as a substitute for digital retina imaging (DRI) in diabetes screening using the MACBETH multicriteria decision-making tool. The research identifies five key evaluation criteria: clinical efficacy, cost-effectiveness, patient needs, user safety, and ethical considerations. A focus group of experienced health technology decision-makers was used to compare NFC and DRI. Results indicate that clinical efficacy and cost-effectiveness are the most critical factors, with NFC showing potential but not significantly outperforming DRI. Sensitivity analyses highlight the need for further research to validate NFC as an effective diabetes screening tool.

An optimized pair-wise comparison approach for automated feature weight assignment in content-based image retrieval system

An optimized pair-wise comparison approach for automated feature weight assignment in content-based image retrieval system

On the Use of Image Analysis for Hematocrit Evaluation in Dried Blood Spots

Dried blood spots (DBSs) are formed by collecting a small sample of blood on specialized filter paper and allowing it to dry naturally. Various domains of life sciences and drug research extensively use DBSs as a sampling technique. The “Hematocrit (Ht) effect” affects assay bias, and several strategies have been put forth to deal with it, including the correction of quantified concentrations using an appropriate correction factor. The approach was previously applied, following the utilization of an image processing algorithm developed in Matlab® to derive a reliable equation correlating DBS areas to Ht% values. The present work looks more closely at the application of image analysis to the evaluation of Ht in DBS samples. Utilizing image analysis software, DBS samples with known Ht values were processed. Preparation of cards has followed a previously developed protocol for the appropriate formation of uniform area DBSs, irrespective of Ht. The resulting areas showed close resemblance to the respective theoretical areas calculated by applying the correlation equation. Following that, the equation was utilized to determine the Ht values for each sample, and a comprehensive comparison of measured versus calculated Ht was carried out using various statistical approaches for method comparison. The results demonstrated a strong correlation, suggesting the method’s viability in estimating Ht for unknown DBS samples.

From Data to Decisions: Analyzing PaRIS for Health Policy Insights

Abstract To inform countries on how well they perform from the perspective of people living with chronic conditions in comparison with other countries, we propose an analytical framework for the data collected in the PaRIS survey, detailing how the data will be analysed in a multilevel approach for cross-country comparison. The data structure of the PaRIS survey represents three levels: countries/health systems, primary care practices and patients. Multilevel analysis is used because of its accuracy in estimating country-level outcomes, its flexibility in modelling relationships, and its opportunities in connecting to relevant policy questions. Country-level outcomes will be estimated to facilitate cross-country comparison and (future) within-country comparison over time. A first indication for areas for cross-country learning and policy making is the distribution of variation over patients, care providers and countries. Characteristics of patients that potentially explain variation in patient-reported outcomes and experiences can be linked to primary care practice and country/health system characteristics. The second indication for areas for cross-country learning and policy making is the variation in the slope of the association between patient and practice characteristics and patient-reported outcomes and experiences. This makes it possible to address policy-relevant questions relating, e.g., to whether some health systems or providers are better able to produce positive outcomes and experiences for (e.g.) older patients, and to the impact of chronic care management on patients with a specific chronic condition. To ensure fair cross-country comparisons, all outcomes will be estimated for an OECD (age and sex standardised) reference population, also including case-mix adjustment where necessary. Analyses will be repeated using a country-specific standard population.

Using Overlap Weights to Address Extreme Propensity Scores in Estimating Restricted Mean Counterfactual Survival Times.

While inverse probability of treatment weighting (IPTW) is a commonly used approach for treatment comparisons in observational data, the resulting estimates may be subject to bias and excessively large variance under lack of overlap. By smoothly down-weighting units with extreme propensity scores, i.e., those that are close (or equal) to zero or one, overlap weighting (OW) can help mitigate the bias and variance issues associated with IPTW. Although theoretical and simulation results have supported the use of OW with continuous and binary outcomes, its performance with survival outcomes remains to be further investigated, especially when the target estimand is defined based on the restricted mean survival time (RMST). We combine propensity score weighting and inverse probability of censoring weighting to estimate the restricted mean counterfactual survival times, and provide computationally-efficient variance estimators when the propensity scores are estimated by logistic regression and the censoring process is estimated by Cox regression. We conduct simulations to compare the performance of weighting methods in terms of bias, variance, and 95% interval coverage, under various degrees of overlap. Under moderate and weak overlap, we demonstrate the advantage of OW over IPTW, trimming and truncation, with respect to bias, variance, and coverage when estimating RMST.

Gaming the System? A Qualitative Exploration of Physician Assistant Learner Perceptions of Virtual Patient Education.

Virtual patients (VPs) are increasingly used in health professions education. How learners engage with VPs and the relationship between engagement and authenticity is not well understood. We explored learners' perceptions of VP education to gain an understanding of the characteristics promoting meaningful engagement in learning, including perceived authenticity. Using a constructivist grounded theory approach, we conducted interviews and focus groups with 11 students from 2 Canadian Physician Assistant programs, where VP learning was implemented to supplement clinical education during the COVID-19 pandemic. We explored trainee perspectives on the use of VPs as an educational modality. Data were iteratively collected and descriptively analyzed thematically using a constant comparison approach until theoretical sufficiency was reached. We identified 3 groups of factors influencing these students' VP learning experiences: (1) technical factors related to the VP platform influenced the perceived authenticity of the patient interactions; (2) individual factors of learners' attitudes influenced their engagement and motivation; and (3) contextual factors related to the learning environment influenced the acceptability and perceived value of the learning experience. Overall, the psychological authenticity of the learning platform and students' motivation for self-directed learning were perceived as most important for students' learning experiences. Implementing VP learning as a supplement to clinical education should be done with consideration of factors that enhance the psychological authenticity of the learning platform, promote learner engagement and accountability, and encourage acceptability of the learning modality through curricular placement and messaging.

Degradation modeling of serial space lithium-ion battery pack based on online inconsistency representation parameters

Establishing an inconsistency-based degradation model for lithium-ion battery packs is crucial for suppressing the degradation of battery packs by optimizing the inconsistency. This paper proposes a method for modeling the degradation of serial space lithium-ion battery packs based on online inconsistency representation parameters. Firstly, the static inconsistency representation parameters are acquired online by quantifying the difference among voltage intervals of cells through Gaussian distribution, addressing the challenge of acquiring static inconsistency representation parameters online. Simultaneously, dynamic inconsistency representation parameters are serialized representations by quantifying the voltage differences of cells at multiple moments, better capturing the rapid evolutionary process of inconsistency. Secondly, a linear model is used to model the serialized parameters and degradation, simplifying the modeling process while achieving multi-source information fitting. Meanwhile, a nonlinear model is constructed to better fit the nonlinear degradation trend of the battery pack. Then, Kalman filter is utilized for model fusion to achieve complementary advantages and improve modeling accuracy. Cross-validation with laboratory battery pack data confirms that this method outperforms comparison approaches, achieving the mean absolute error and maximum error of less than 1.73 % and 3.31 %, respectively. The method proposed in this paper provides a basis for future work on battery pack life extension.

Strengths and weaknesses of EU regional reactivity to shocks

This study introduces the novel concept of regional reactivity to shocks. A region is considered to be much more reactive if it bounced back to the level of labour productivity achieved before a shock in the same or less time than it took to reach the pre-2008 economic crisis peak from an equivalent lower bound. The analysis of the reactivity of the EU-NUTS2 regions reveals a clear spatial pattern. By using a spatial lag model selected via a Bayesian comparison approach, we show that tertiary education and institutional quality are key to promote reactivity. On the other hand, population density acts in the opposite direction. Our results are potentially useful for defining policy strategies that emphasise or refocus the strengths of each region in light of current territorial trends and emerging challenges.

Formation of Centers of Excellence in Russia for Stimulating Industrial Development

The purpose of the study is to summarize the experience of forming Centers of Excellence in the USSR and the Russian Federation for the period from 1920 to the present on the basis of triune contexts. The research objectives are to identify the key tools for the formation and development of Centers of Excellence and compare the results of their use in order to identify the directions for improving state policy and the mechanism for strengthening them, taking into account the current economic situation and the strategic prospects for the country's development. The following research methods have been applied, namely: induction, deduction, analysis and synthesis, observation, comparison, systematization, description, empirical, evolutionary, systemic and historical-logical approach to the analysis of certain phenomena and processes. The results of the study have revealed the most effective mechanisms and tools for forming and stimulating the development of Centers of Excellence for the period from the 1920s to the present, and the paper also presents their evolution based on the use of triune contexts. The analysis has shown that it is advisable to take into account the experience of the USSR when developing the mechanism and selecting tools for the formation of Centers of Excellence in modern Russia.

Dynamic Load Balancing of Multi-GPU Parallelization for VULCANO VE-U7 Corium Spreading Analysis Using SOPHIA

The SOPHIA code is a graphical processing unit (GPU)–based smoothed particle hydrodynamics (SPH) framework for fundamental nuclear thermal-hydraulic and safety applications developed by Seoul National University. Focusing on particle-based SPH methods, the SOPHIA code provides capabilities for computational fluid dynamics–level analysis in addressing complex multiphase and multiphysics issues related to severe accidents and reactor safety. Since the SPH method requires high computational cost due to the nature of the particle-based method, the SOPHIA code was parallelized with multiple GPUs. However, using multiple GPUs may cause load imbalance as particles migrate between GPUs, which degrades performance. In this study, we developed a dynamic load-balancing algorithm to increase computational efficiency by reducing the load imbalance. Additionally, a novel cumulative time comparison approach is proposed as a criterion for adjusting boundaries between GPUs, ensuring low load imbalance. To evaluate the performance of the algorithm, three-dimensional dam-break simulations were conducted. The results demonstrated enhanced computational efficiency and equitable load distribution among GPUs compared to using fixed subdomains. Additionally, the developed algorithm was utilized to analyze the VULCANO VE-U7 experiment, and the results were well aligned with the experimental data. Therefore, the practical applicability of the proposed dynamic load-balancing algorithm in nuclear safety analyses has been confirmed.

A comparison of ore pre-concentration efficiency between two high voltage pulse generator systems

High Voltage Pulse (HVP) enabled ore pre-concentration can potentially provide large energy savings and the ability to upgrade low-grade ores for the mining industry. Two types of pulse generator systems commonly used in HVP breakage are Marx generators and pulse transformers. This initial assessment compares the pre-concentration performance of a SelFrag Lab Unit (utilising a Marx generator) and a custom-built HVP machine (employing a pulse transformer) from Huazhong University of Science and Technology (HUST). Eight sets of experimental results using the two generator systems were collected. Two approaches were taken to analyse the data; a trendline approach using all eight sets of data, and a single-point comparison approach with matched specific energy inputs. The results show that there are significant differences in pre-concentration efficiencies with the HUST machine performing better. To elucidate the potential causes, the degree of size reduction (t10), breakage probability, and selective breakage were investigated for the two generator systems. It is likely that the efficiency of selective breakage is one of the major causes, with the HUST machine presenting better results at selectively breaking high-grade particles and consuming less energy. Additionally, the two generator systems have different energy per discharge levels; the effect of this difference should be investigated in future work.

Application of Strain Elastography in Dentistry: A Systematic Review.

Strain elastography, a non-invasiveimaging technique complements traditional diagnostic methods by offering quantitative and qualitative information about soft and hard tissues within the oral cavity. The article aimed toprovide an overview of the currently available data on the use of strain elastography in dentistry. To support the review of strain elastography applications in dentistry, a wide range of articles was searched using both online and offline databases. Inclusion and exclusion criteria were defined according to the Population, Intervention, Comparison, Outcomes, and Study Design (PICOS) approach. The results show that 12 of the 107 papers found to be eligible for inclusion in a qualitative examination of the use of strain elastography in dentistry satisfied the PICOS criteria. Elastographyis a promising tool for diagnosing various dental diseased conditions, but sufficient evidence is not available. More studies on a larger population should be performed to determine its accuracy in diagnosis.