Number Of Classes Research Articles

How to Maximize Minimum Number of Colour Classes for Edge Colouring Using Families of Disjoint Sets Colouring Technique

In this paper we use families of disjoint sets colouring technique, to introduce some results of edge colouring for minimum and maximum number of colour classes, and introduce a results explains how to find different values of minimum colour classes, and how to maximize minimum number of colour classes for edge colouring, when maximum degree of vertex is fixed, here we prove a result as a method for finding minimum number of colour classes for edge colouring. Families of disjoint sets colouring technique, as generalization technique deal even with graph of multiple edges, and here graph of multiple edges given as application of some examples and results.

Association between Antibiotic Use and Subsequent Risk of Breast Cancer: A Nationwide Retrospective Cohort Study in South Korea.

Several studies have revealed a possible association between antibiotic use and breast cancer in the Western population of women. However, its association with the Asian population remains unclear. Data utilized in this nationwide population-based retrospective cohort study was obtained from the Korean National Health Insurance Service (NHIS) database. The study population consisted of 4,097,812 women who were followed up from January 1, 2007, to December 31, 2019. Cox proportional hazards regression was utilized to calculate adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for the risk of breast cancer according to cumulative days of antibiotic use and the number of antibiotic classes used. It was discovered that women who used antibiotics for more than 365 days had a higher risk of breast cancer (aHR, 1.15; 95% CI, 1.09-1.21) in comparison to those who did not use antibiotics. In addition, an association was found among women who used five or more classes of antibiotics, showing a higher risk of breast cancer (aHR, 1.11; 95% CI, 1.05-1.17) compared to non-users. Furthermore, compared to antibiotic non-users, only users of cephalosporins (aHR 1.09; 95% CI, 1.02-1.17) and lincosamides (aHR 1.70; 95% CI, 1.20-2.42) had a higher risk of breast cancer. These findings support epidemiological evidence that long-term use of antibiotics may be associated with a higher risk of breast cancer. This underscores the need for further studies to address the potential for residual confounding, confirm causation, and elucidate the underlying mechanisms.

Simplified ischemia-free liver transplantation by providing alternating flow of the portal vein and hepatic artery: Applicability, efficiency, safety.

Conventional ischemia-free liver transplantation (CIFLT) represents a pioneering procedure that circumvents ischemia-reperfusion injury (IRI) to livers throughout the transplant procedure. However, its complexity has limited its widespread adoption. This study introduced a simplified ischemia-free liver transplantation (SIFLT) technique by providing alternating flow of the portal vein and hepatic artery, demonstrating its efficacy and safety. In this retrospective study, 32 patients who received SIFLT and 32 who received CIFLT were included between January 2021 and January 2024. The intraoperative and postoperative outcomes were collected and elevated. Patients who underwent SIFLT had a shorter anhepatic phase (44.0±2.4 mins vs 51.6±2.4 mins, p=0.03), along with a comparatively reduced intraoperative blood loss compared to those underwent CIFLT. Furthermore, the SIFLT group exhibited significantly lower peak AST levels within postoperative 7 days (413.1±62.6U/L vs 707.5±110.7U/L, p=0.02). The incidence of early allograft dysfunction, primary non-function, transplant-related complications were similar across both groups. There were no significant differences in number of Clavien-Dindo classification of surgical complication and comprehensive complication index at 3-month after transplantation. Kaplan-Meier analysis confirmed similar patient and graft survival rates. The subgroup analysis of extended criteria donors demonstrated that SIFLT can effectively reduce anhepatic phase and intraoperative blood loss, and can achieve clinical prognosis similar to CIFLT. Additionally, histological analysis revealed that both groups demonstrated well-preserved liver and bile duct. The SIFLT simplifies the intricate surgical procudure while ensuring protection of livers from IRI. This technique holds promise for enabling patients to achieve clinical outcomes comparable to those of CIFLT.

Association of Duration of Recognized Hypertension and Stroke Risk: The REGARDS Study.

The focus for reducing hypertension-related cardiovascular disease is the management of blood pressure. Limited data are available on the potential benefit of delaying the onset of hypertension. Stroke-free Black and White participants from the REGARDS cohort study (Reasons for Geographic and Racial Differences in Stroke; recruited 2003-2007) were followed through 2022 for incident stroke events. Participants were stratified by duration of recognized hypertension: normotensive (0 years), ≤5 years, 6 to 20 years, or 21+ years. The baseline systolic blood pressure (SBP), the number of classes of antihypertensive medications, and the risk of incident stroke were assessed by duration strata adjusting for demographics, cerebrovascular risk factors, SBP, and use of antihypertensive medications (where appropriate). Of 30 239 study participants, we included 27 310 participants (mean age, 65 years; 45% male), followed a median of 12.4 years, during which 1763 incident stroke events occurred. On average, participants with hypertension duration ≤5 years, 6 to 20 years, and 21+ years were taking 1.68 (95% CI, 1.65-1.71), 2.04 (95% CI, 2.01-2.07), and 2.28 (95% CI, 2.25-2.31) classes of antihypertensive medications, respectively. The adjusted mean SBP level was higher with each increasing duration of recognized hypertension (0, ≤5, 6-20, and 21+ years): 123.9 mm Hg (95% CI, 123.3-124.6), 129.7 mm Hg (95% CI, 129.1-130.2), 131.7 mm Hg (95% CI, 130.6-131.5), and 132.6 mm Hg (95% CI, 132.0-133.1). Compared with normotensive individuals, the hazard for incident stroke increased from 1.31 (95% CI, 1.05-1.63) for ≤5 years duration, 1.50 (95% CI, 1.21-1.87) for 6 to 20 years duration, and 1.67 (95% CI, 1.32-2.10) for 21+ years duration. Longer duration of recognized hypertension was associated with more classes of antihypertensive medications, higher mean SBP, and higher stroke risk even after adjustment for age and SBP. Collectively, this suggests that delaying the onset of hypertension could reduce the burden of stroke.

Financial Markets, Efficiency, and Credit Intermediation

Abstract Research and teaching in financial economics very often assumes that the efficient market hypothesis is verified. Nonetheless, financial markets are still very far from reaching substantial levels of efficiency. Moreover, different perspective of market efficiency can be subsumed. In this paper, we provide a clearcut taxonomy of the different nuances of financial markets efficiency, useful for economic research and teaching. We also argue that scholars should necessarily disclose in advance the perspective of market efficiency assumed for the research analysis or teaching purposes and discuss the limitations in the inference that can derive. JEL classification numbers: G14, G10. Keywords: Financial markets, Market efficiency, Efficient market hypothesis, EMH.

Writing and reading workshop: impact on reading motivation, reading amount and text comprehension

AbstractThe article explores the relationship between intrinsic and extrinsic reading motivation, reading amount and the comprehension of narrative and expository texts in lower secondary school students. In addition, we intended to verify the effect on these variables of an educational programme, the writing and reading workshop (WRW), aimed at promoting students' love of reading and useful reading strategies. The sample included 149 children attending the first year of lower secondary school. A longitudinal design with two steps of data collection was used. In five classes, teachers applied the WRW; the same number of classes were enrolled as a control group and the teachers applied the Italian traditional teaching programme. The experimental group achieved better results in narrative text comprehension and in reading amount after 1 year. Reading amount was found to mediate the relationship between the instructional method and reading comprehension. No effects of the teaching method were found on either intrinsic or extrinsic motivation.

The Relationship between Investment Horizons and Signals of Insider Trading in Takeover

Abstract Insider trading in target companies prior to mergers and acquisitions (M&A) can be regarded as an important information about the future profitability of the company. Suk and Wang (2021) find that net purchases by insiders in target companies are positively correlated with abnormal returns at the time of the M&A announcement, M&A synergy, bid premiums, and the probability of M&A completion. Fu et al. (2020) document that long-term investment horizon shareholders can impede insider trading because they can obstruct insiders from using their informational advantages through direct supervision. In this paper, we find that the positive impact of net insider purchases in target companies on abnormal returns at the time of the M&A announcement is stronger in companies with shorter shareholder investment horizons. However, there is no evidence to support that net insider purchases in target companies have a stronger positive impact on M&A synergy in companies with shorter shareholder investment horizons. Furthermore, there is no evidence to indicate that net insider purchases in target companies have an impact on bid premiums, nor that this impact is stronger in companies with shorter shareholder investment horizons. Moreover, we find that the positive impact of net insider purchases in target companies on the probability of M&A completion is stronger in companies with shorter shareholder investment horizons. JEL classification numbers: G11, G14, G34. Keywords: Insider trading, Shareholder investment horizon, Insider investment horizon, Acquisition.

Class number for pseudo-Anosovs

Given two automorphisms of a group G G , one is interested in knowing whether they are conjugate in the automorphism group of G G , or in the abstract commensurator of G G , and how these two properties may differ. When G G is the fundamental group of a closed orientable surface, we present a uniform finiteness theorem for the class of pseudo-Anosov automorphisms. We present an explicit example of a commensurably conjugate pair of pseudo-Anosov automorphisms of a genus 3 3 surface, that are not conjugate in the mapping class group, and we also show that infinitely many pairwise non-commuting pseud-Anosov automorphisms have class number equal to one. In the appendix, we briefly survey the Latimer-MacDuffee theorem that addresses the case of automorphisms of Z n \mathbb {Z}^n , with a point of view that is suited to an analogy with surface group automorphisms.

The Effect of Sustainable Development Goals on Customer Satisfaction, Customer Loyalty and the Role of Brand Image in Green Hotels

Abstract The study delves into the interplay between Sustainable Development Goals (SDGs) and their influence on customer satisfaction, brand image, and loyalty in the green hotel industry. It highlights the significance of environmentally responsible practices as a key driver of brand perception and customer trust. The research demonstrates how SDG-centric strategies not only enhance a hotel's environmental profile but also bolster its market position by aligning with evolving customer values. This alignment is shown to be imperative in the hospitality sector, where customer choices are increasingly swayed by social responsibility and environmental stewardship. Furthermore, the study explores the dynamics between a strong, SDG-driven brand image and its cascading effects on customer loyalty, underlining the economic and ethical imperatives of sustainable practices in the hotel industry. It provides a comprehensive analysis of the reciprocal relationship between sustainable business practices and customer perception, offering valuable insights for hoteliers seeking to integrate SDGs into their operational models to foster customer loyalty and drive long-term success. JEL classification numbers: F64, M14, M31. Keywords: SDGs, CSR, Green development, Customer satisfaction, Customer loyalty, Brand image.

AN APPROACH WITH DEEP CONVOLUTIONAL NEURAL NETWORKS FOR ACCURATE ARCHITECTURAL STYLE CLASSIFICATION

Unlike other studies, this study aims to examine the effect of data diversity and quantity on the performance of a model rather than solely focusing on classifying architectural styles. It also seeks to emphasize the importance of selecting the appropriate number of classes in architectural style classification and to explore how determining the optimal number of classes affects the model's success. The first dataset comprises the original images (4776 images), the second dataset (10091 images) includes additional data and the third dataset (9552 images) is obtained through data augmentation. Using these three datasets, models were developed by reducing the number of classes with convolutional neural networks (CNNs), which utilize convolution layers to identify local features in the images and classify them by summarizing these features. The results of the developed models show that the method of adding data and increasing data decreased the success of architectural style classification, whereas reducing the number of styles increased it. This study may lay the groundwork for the development of deep learning models for future architectural style classification. The findings of this study can significantly impact various application areas, such as analyzing architectural styles, identifying historical periods and integrating them into architectural education.

Exploring neural architectures for simultaneously recognizing multiple visual attributes

Much experimental evidence in neuroscience has suggested a division of higher visual processing into a ventral pathway specialized for object recognition and a dorsal pathway specialized for spatial recognition. Previous computational studies have suggested that neural networks with two segregated pathways (branches) have better performance in visual recognition tasks than neural networks with a single pathway (branch). One previously proposed possibility is that two pathways increase the learning efficiency of a network by allowing separate networks to process information about different visual attributes separately. However, most of these previous studies were limited, considering recognition of only two visual attributes, identity and location, simultaneously with a restricted number of classes in each attribute. We investigate whether it is always advantageous to use two-pathway networks when recognizing other visual attributes as well as examine whether the advantage of using two-pathway networks would be different when there are a different number of classes in each attribute. We find that it is always advantageous to use segregated pathways to process different visual attributes separately, with this advantage increasing with a greater number of classes. Thus, using a computational approach, we demonstrate that it is computationally advantageous to have separate pathways if the amount of variations of a given visual attribute is high or that attribute needs to be finely discriminated. Hence, when the size of the computer vision model is limited, designing a segregated pathway (branch) for a given visual attribute should only be used when it is computationally advantageous to do so.

Stocks, Gold and Crude Oil: How Valuable are Volatility and Correlation Timing?

Abstract We study the sources of portfolio returns under parameter uncertainty arising from allocation strategies based on holdings of the NASDAQ index, WTI crude oil and gold. We investigate the contribution of volatility and correlation forecasts for dynamic portfolio allocations in the model of Brandt, Goyal, Clara, and Stroud (2005). We estimate alternative forecasting models using Bayesian methods, and evaluate investor’s utility under the Bayesian predictive density. We then compare the performance of different portfolio strategies through both Sharpe ratios and utility-based metrics. We show that dynamic strategies based on timing volatilities and correlations can add positively to the economic gains generated by non-diversified portfolios involving holdings of either crude oil or gold only. Hence, the economic benefits generated by holding crude oil and gold in asset allocations with stocks arise from the predictability of their volatilities and correlations. JEL classification numbers: C22, C52, G11, G23. Keywords: Bayesian models, dynamic asset allocation, multivariate correlation models, crude oil, gold.

The Predictive Power of the Yield Spread Under the Veil of Time

Abstract This paper studies the sources of cyclical information delivered by the term spread for output growth predictability in the U.S. I use a wavelet-based time-frequency decomposition to decompose the predictive power of the yield spread across time scales, both in-sample and out-of-sample, over various forecast horizons. Spreads between interest rates on 10-year and 3-month Treasuries have a predictive ability for output growth that changes largely over different time scales. I find evidence of a negative correlation between the spread and future GDP growth for fluctuations with a frequency of 4 to 8 years per cycle. A linear combination among filtered yield spreads shows a sizable improvement in forecasting out-of-sample. The time-frequency decomposition is also used to propose an interpretation for the breakdown of in-sample predictability documented by Dotsey (1998) that arises after 1985. JEL classification numbers: C19, E43, E27. Keywords: Multiresolution analysis, Term structure, Predictability.

Unsupervised machine learning in financial anomaly detection: clustering algorithms vs. dedicated methods

The article presents the application of selected clustering algorithms for detecting anomalies in financial data compared to several dedicated algorithms for this problem. To apply clustering algorithms for anomaly detection, the Determine Abnormal Clusters Algorithm (DACA) was developed and implemented. This parameterized script (DACA) allows clusters containing anomalies to be automatically detected on the basis of defined distance measures. This kind of operation allows clustering algorithms to be quickly and efficiently adapted to anomaly detection. The prepared test environment has allowed for the comparison of selected clustering algorithms. K-Means, Hierarchical Cluster Analysis, K-Medoids, and anomaly detection: Stochastic Outlier Selection, Isolation Forest, Elliptic Envelope. The research has been carried out on real financial data, in particular on the income declared in the asset declarations of the targeted professional group. The experience of financial experts has been used to assess anomalies. Furthermore, the results have been evaluated according to a number of popular classification and clustering measures. The highest result for the investigated financial problem was provided by the K-Medoids algorithm in combination with the DACA script. It is worthwhile to conduct future research on the introduced solutions as an ensemble method.

DRCmpVis: Visual Comparison of Physical Targets in Mobile Diminished and Mixed Reality.

Numerous physical objects in our daily lives are grouped or ranked according to a stereotyped presentation style. For example, in a library, books are typically grouped and ranked based on classification numbers. However, for better comparison, we often need to re-group or re-rank the books using additional attributes such as ratings, publishers, comments, publication years, keywords, prices, etc., or a combination of these factors. In this article, we propose a novel mobile DR/MR-based application framework named DRCmpVis to achieve in-context multi-attribute comparisons of physical objects with text labels or textual information. The physical objects are scanned in the real world using mobile cameras. All scanned objects are then segmented and labeled by a convolutional neural network and replaced (diminished) by their virtual avatars in a DR environment. We formulate three visual comparison strategies, including filtering, re-grouping, and re-ranking, which can be intuitively, flexibly, and seamlessly performed on their avatars. This approach avoids breaking the original layouts of the physical objects. The computation resources in virtual space can be fully utilized to support efficient object searching and multi-attribute visual comparisons. We demonstrate the usability, expressiveness, and efficiency of DRCmpVis through a user study, NASA TLX assessment, quantitative evaluation, and case studies involving different scenarios.

EA-CNN: Enhanced Attention-CNN with Explainable AI for Fruit and Vegetable Classification

The quality of vegetables and fruits are judged by their visual features. Misclassification of fruits and vegetables lead to a financial loss. To prevent the loss, superstores need to classify fruits and vegetables in terms of size, color and shape. To improve the accuracy of models for classifying fruits and vegetables, researchers have introduced various CNN architectures like VGG16, AlexNet, DenseNet-121 and different attention-based variants by compromising models’ complexity leading to high computational cost and overlooking feature’s contribution for interpretable predictions. Additionally, the major drawback in most of the existing works is the utilization of the limited dataset and number of classes for fruit and vegetable classification task instead of the benchmark dataset like Fruit-360 which comprises of 141 distinct fruit and vegetable classes. In this study, an explainable artificial intelligence (XAI) driven enhanced attention-CNN (EA-CNN) is proposed for accomplishing the fruit and vegetable classification task accurately and efficiently. The proposed EA-CNN model (a) detects the class of fruits and vegetable accurately through visual features by manipulating undiscover customized pooling technique and enhanced attention feature extraction mechanism, (b) classifies the fruits and vegetables efficiently through a simplified architecture, (c) provides interpretable predictions through XAI approach. By utilizing entire fruit-360 database, this research noteworthily enhances the model’s capability to classify wide range of fruits and vegetables, thus providing an effective and reliable solution for practical applications. The proposed study outclasses baseline models with regard to accuracy and computational cost on fruit-360 benchmark dataset. EA-CNN achieves better accuracy of 98.1% in smaller number of iterations as compared to benchmark models. Moreover, to further prove the efficiency, generalization ability, robustness, scalability and adaptability of this research, the architecture of proposed EA-CNN model is validated on another real-world dataset named as ‘Fruit Recognition’. The experimental outcomes display that the proposed EA-CNN model produces substantial performance on Fruit-Recognition dataset as well by attaining a generalized accuracy of 96%.

Explainable AI for News Classification

Abstract: The proliferation of news content across digital platforms necessitates robust and interpretable machine learning models to classify news into predefined categories effectively. This study investigates the integration of Explainable AI (XAI) techniques within the context of traditional machine learning models, including Naive Bayes, Logistic Regression, and Support Vector Machines (SVM), to achieve interpretable and accurate news classification. Utilizing the News Category Dataset, we preprocess the data to focus on the top 15 categories while addressing class imbalance challenges. Models are trained using Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, achieving an acceptable classification accuracy of 67% across all models despite the complexity introduced by the high number of classes. To elucidate the decision-making processes of these models, we employ feature importance visualizations derived from model coefficients and feature log probabilities, complemented by local interpretability techniques such as LIME (Local Interpretable Model-agnostic Explanations). These methodologies enable granular insights into word-level contributions to predictions for each news category. Comparative heatmaps across models reveal significant consistencies and divergences in feature reliance, highlighting nuanced decision-making patterns. The integration of explainability into news classification provides critical interpretive capabilities, offering transparency and mitigating the risks associated with algorithmic opacity. The findings demonstrate how XAI enhances stakeholder trust by aligning model predictions with human interpretability, particularly in ethically sensitive domains. This work emphasizes the role of XAI in fostering responsible AI deployment and paves the way for future advancements, including deep learning integration and multilingual news classification with inherent interpretability frameworks

AI 기술이 제공하는 서비스와 서비스 무역 규범에 대한 논의

Regarding whether GATS applies to services provided by AI technology, Article 1.1 of GATS declares that this Agreement applies to measures of Members affecting trade in services, focusing on “measures.” Since GATS applies to all trade in services regardless of the means of supplying services, supplying services electronically falls within the scope of GATS. In addition, GATS does not have provisions distinguishing between various technological means of supplying services, so services provided by AI technology are also included within the scope of GATS. AI is essentially software, and software is a service, so AI falls under the “Computer and Related Services” sector under the 1991 W/120 classification. Under the current CPC Version 3.0, AI will be specified under the subsector “Application software provision” and “On-line software”. GATS prepared its service concessions under the 1991 W/120 classification, and this classification has been aligned with the corresponding CPC classification numbers. Since this classification document was prepared in 1991, once the CPC Version 3.0 revision work is complete, the classification document will need to be revised to align with the new CPC classification numbers. Service-driven AI technology are imported when purchasing subscriptions from foreign-owned or foreign-based LLMs providers, and then a service trade occurs. Purchasing data labeling or data annotation services from foreign-owned or foreign-founded companies is also importing services. This type of service trade is likely to occur through Mode 1 among the service supply modes. On the other hand, when AI technology is installed in a product, such as a self-driving car, and the product is imported, and the product exists in the country where the consumer resides and provides the service, the product and the service are provided and consumed simultaneously, and the product acts as a kind of “service provider” at the moment the consumer consumes the service, which may differ from the WTO’s regulatory system. Therefore, there have been attempts to recognize the form of service trade included or embodied in a product as Mode 5, and to recognize it as a new supply of service in GATS, but it has not been formally discussed at the WTO level. The trade method in the form of Mode 5 is similar to Mode 1, which is the cross-border supply of services, so service trade is treated as Mode 1, and when services are integrated with products and trade occurs, the service no longer exists and the GATT, which is the rule applied to products, is applied.

FEISTY Fortran library and R package to integrate fish and fisheries with biogeochemical models

Abstract The FishErIes Size and functional TYpe model (FEISTY) is a mechanistic ecosystem model that fully integrates ecosystem structure across trophic levels through functional types. We present an R package that enables users to run simulations ranging from a 0D chemostat to full global scales. The library is written in Fortran90 with an R interface and provides a web application for visual exploration. We present and compare results from four core configurations across a range of depths, productivity and fishing levels, and we assess the convergence of solutions as the number of size classes is increased. The model has historically been coupled to biogeochemical models of mesozooplankton and detritus production, but it can also be applied in a stand‐alone version. We demonstrate the library to set up and simulate fish communities under varying productivity of mesozooplankton and benthos, and top‐down forcing from fishing. We outline three strategies for coupling FEISTY with biogeochemical model output and discuss future directions and open issues.

On the enumeration of orbits of unipotent groups over finite fields

We show that the enumeration of linear orbits and conjugacy classes of Z \mathbf {Z} -defined unipotent groups over finite fields is “wild” in the following sense: given an arbitrary scheme Y Y of finite type over Z \mathbf {Z} and integer n ⩾ 1 n\!\geqslant \! 1 , the numbers # Y ( F q ) mod q n \#Y(\mathbf {F}_q) \bmod q^n can be expressed, uniformly in q q , in terms of the numbers of linear orbits (or numbers of conjugacy classes) of finitely many Z \mathbf {Z} -defined unipotent groups over F q \mathbf {F}_q and finitely many Laurent polynomials in 𝑞.