Sample Selection Research Articles

Pengaruh Konten Pemasaran dan Kualitas Produk Terhadap Minat Beli Ulang pada Toko Aladoy di Shopee

Technological progress is inevitable in life following the progress of the times and science, especially in the women's clothing sector, marketing content and product quality play an important role in attracting consumers' buying interest again. The phenomenon of increasing progress in innovation is influencing people's shopping behavior: consumers who usually shop offline are now shopping online. The Aladoy store on Shopee offers an interesting marketing content feature that the company calls live streaming as live online shopping. The aim of this research is to examine how marketing content and product quality influence consumers' willingness to repurchase. The sample selection amounted to 180 respondents using the purposive sampling method. Data was collected by distributing questionnaires. This research uses quantitative and technical data analysis methods in the form of descriptive analysis, data processing uses Partial Least Square (PLS). The results of this research show that marketing content and product quality influence the interest in repurchasing women's clothing products at the Aladoy store on Shopee.

Pengaruh Personal Identity, Social Identity, Kredibilitas Influencer Terhadap Green Purchasing Behavior

This research tests and analyzes personal identity, social identity, influencer credibility on green purchasing behavior. And knowing which variables are the most dominant in influencing green purchasing behavior. The research uses a quantitative approach that uses causal relationships. The sample selection criteria in this study were aged 12 years to 43 years who had purchased and used Tisoo brand tissue and were in the Jabodetabek area. Questionnaires were collected from 100 respondens, the data was then processed using the multiple linear regression method. Based on the results of the analysis, it is known that the personal identity variable partially has a positive effect on green purchasing behavior. Meanwhile, the social identity variable and the influencer credibility variable partially have a negative effect on green purchasing behavior. Simultaneous hypothesis testing states that personal identity, social identity and influencer credibility have a positive effect on green purchasing behavior. and the most dominant variable in influencing green purchasing decisions is the personal identity variable. The implication is that companies like Tisoo should emphasize environmental values in their marketing and select influencers who understand eco-friendly products to encourage green purchasing.

Pengaruh Harga dan Kualitas Produk Terhadap Keputusan Pembelian pada Kentucky Fried Chicken (KFC) di Cabang Karawang Galuh Mas

The purpose of the study was to analyze purchasing decisions at Kentucky Fried Chicken (KFC) at the Karawang Galuh Mas branch which can be influenced by price and product quality. Using quantitative methods, through a focus on price and product quality variables. For sampling techniques, this study used purposive sampling, selecting samples based on specific criteria in accordance with research needs. Namely KFC consumers at the Karawang Galuh Mas branch. Data for this study were obtained through a questionnaire distributed to 96 respondents, then with a sample selection based on the Lemeshow formula. Data analysis was carried out through multiple regression to understand the relationship between variables thoroughly. While the classical assumption tests used in this study include normality test, multicollinearity test, and heteroscedasticity test with the help of the SPSS (Statistical Product and Service Solutions) version 27 program. The results of this study indicate that price and product quality have a positive and significant effect on purchasing decisions at Kentucky Fried Chicken (KFC) at the Karawang Galuh Mas branch.

The carbon emission reduction effect of green fiscal policy: a quasi-natural experiment

Carbon emission reduction is crucial for mitigating global climate change, and green fiscal policies, through providing economic incentives and reallocating resources, are key means to achieve carbon reduction targets. This paper uses data covering 248 cities from 2003 to 2019 and applies a multi-period difference-in-differences model (DID) to thoroughly assess the impact of energy conservation and emission reduction (ECER) fiscal policies on enhancing carbon emission (CE1) reduction and carbon efficiency (CE2). It further analyzes the mediating role of Green Innovation (GI), exploring how it strengthens the impact of ECER policies. We find that: (1) ECER policies significantly promote the improvement of carbon reduction and CE2, a conclusion that remains robust after excluding the impacts of concurrent policy influences, sample selection biases, outliers, and other random factors. (2) ECER policies enhance CE1 reduction and CE2 in pilot cities by promoting green innovation, and this conclusion is confirmed by Sobel Z tests. (3) The effects of ECER policies on CE1 reduction and the improvement of CE2 are more pronounced in higher-level cities, the eastern regions and non-resource cities. This research provides policy makers with suggestions, highlighting that incentivizing green innovation through green fiscal policies is an effective path to achieving carbon reduction goals.

Pengaruh Kompensasi dan Kepuasan Kerja Terhadap Turnover Intention PT. Renesola Distribusi Indonesia

The aim of this research is to determine the problem of increasing turnover intention at PT. Renesola Distribution Indonesia. This research tries to analyze and determine how compensation and job satisfaction can be related to the increase of turnover intention of PT Renesola Distribution Indonesia employees. The data used in this research was obtained with purposive sampling method directed from PT. Renesola Distribution Indonesia production division worker class with a total of 50 employees, which the sample selection criteria is the same amount of compensation. The data method used is multiple linear regression using IBM SPSS 27. Data collection was carried out by distributing questionnaires to the company employees. The results of this research show that the variables compensation and job satisfaction simultaneously have a negative effect on the employes turnover intention of PT Renesola Distribution Indonesia. The Compensation variable partially has a significant negative influence on employes turnover intention of PT Renesola Distribution Indonesia The partial job satisfaction variable has a significant negative influence on employes turnover intention of PT Renesola Distribution Indonesia.

Improving ML-based landslide susceptibility using ensemble method for sample selection: a case study of Kangra district in Himachal Pradesh, India.

Developing effective strategies to predict areas susceptible to landslides and reducing risk is vital. This involves using ensemble methods to meet the precise prediction and addressing challenges like data limitation. Recent studies have highlighted the potential of using ensemble methods to enhance the prediction of landslide susceptibility maps (LSM). Ensemble methods present a sampling of landslides and non-landslide points from high and low susceptible areas, respectively. Extensive research has explored their application in machine learning processes, particularly in classification-related problems. This study delves into strategies of ensemble as a promising method in future landslide applications. The proposed method was tested considering Kangra district of Himachal Pradesh as study area where three datasets were prepared consisting of presence and absence points. Dataset 1 consisted of initial landslide and randomly generated non-landslide points. In dataset 2, additional landslide points obtained from the very high susceptibility of initial LSM were supplemented with initial landslide data, while the non-landslide points were generated randomly from the study area. Finally, dataset 3 was composed of the landslide points as in dataset 2, and the non-landslide points were obtained from the very low susceptible areas of initial LSM. These datasets are used with random forest (RF) and support vector machine (SVM), thereby preparing six landslide susceptibility maps. To analyze the applicability of the proposed method, we have used metrics such as AUC-ROC, precision, recall, F-score, accuracy and Mathew's correlation coefficient (MCC). The AUC for dataset 1 with SVM and RF is 0.89, which increased to 0.898 and 0.952 for datasets 2 and 3 with SVM and 0.937 and 0.954 with RF. Among all the methods, the precision and recall values were highest for dataset 3 with SVM as well as RF. Hence, based on several accuracy metrics, we conclude that when the landslides and non-landslides samples were sampled from very high and very low susceptible areas respectively, the LSM performed better than all the other methods. Sampling landslides from very high susceptible areas only (dataset 2) does not perform well thereby committing misclassification error. The study demonstrated that the landslide and non-landslide data were obtained from very high and very low susceptibility; the predictive capability of the LSM increased significantly. Thus, the results demonstrate the effectiveness of the proposed ensemble approach in providing precise delineation of landslide zones, facilitating informed decision-making for land and hazard management.

Pengaruh Self Congruity, Perceived Price, Brand Trust, dan Customer Engagement terhadap Willingness to Continue and Subscribe pada Disney Plus Hotstar

The advancement of online streaming media and the growing interest of the Indonesian population in Video on Demand (VoD) applications have paralleled the development of the internet in Indonesia. The presence of various video streaming platforms with local, regional, and global reach operating in Indonesia has generated significant dynamics in the digital entertainment industry. This phenomenon has rapidly transformed the landscape, fostering the adoption of technology and altering consumer behavior patterns in accessing entertainment content. This research is aimed at uncovering the relationship between self-congruity and perceived price through various variables such as customer engagement and brand trust, which have implications on willingness to continue and subscribe. The study employs a quantitative research method or survey approach. Sample selection is conducted through non-probability sampling using purposive sampling techniques, with a total sample of 146 respondents who are customers of Disney+ Hotstar Indonesia. Data analysis is performed using Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach. The findings of the research reveal positive and significant influences, namely, self-congruity on brand trust, self-congruity on customer engagement, perceived price on customer engagement, brand trust on willingness to continue and subscribe, and customer engagement on willingness to continue and subscribe.

RETROSPECTİVE INVESTİGATİON OF PATİENT FALLS IN A UNİVERSITY HOSPİTAL WİTHİN THE SCOPE OF PATİENT SAFETY

This study was designed to investigate patient falls in a university hospital within the scope of patient safety according to variables such as age, gender, time interval when the fall occurred, clinic/polyclinic etc. where the fall occurred and the scene of the fall. Our study is a descriptive type restrospective study. The population of the study consisted of 280 inpatient/outpatient fall event notification records reported between 01.01.2020-31.12.2023 in a university hospital in Izmir. No sample selection was made in the study and the entire population was included in the sample. Institutional permission and ethics committee approval were obtained before the research. Analyses were performed with the SPSS Statistics 29 program. Data were analysed by descriptive statistics (number, percentage, mean, standard deviation), correlation test and Pearson chi-square test. The mean age of the patients who fell was 60.07±22.81 years, the majority were male (58.2%), and the age range of 65 years and older was the age group with the highest number of falls (52.5%). Among the patients, 91.1% were in the adult group and 8.9% were in the paediatric group. It was determined that patients fell most frequently while getting out of bed (45.4%) and in the patient room (54.6%). It was found that 78.9% of the patients had no damage as a result of the fall, 20.7% had mild injury, and 0.4% had severe injury. The fall rates of the patients according to years varied between 0.02% and 0.07%, and the highest number of falls occurred in the adult emergency department. It was determined that 22.1% of the falls occurred between 00:01-04:00. When the fall rates by branches were examined, it was observed that paediatrics and emergency department were lower than other branches (8.6%, 8.9%, respectively). It was observed that 54.3% of the patients who fell had a high pre-fall risk assessment. We determined that patients most frequently fell in the patient room while trying to get out of bed. Incident reporting systems that contain all the information needed about patient falls may provide the opportunity for corrective action for incidents that may occur due to falls. Educational interventions to increase the competence of healthcare professionals and preventive interventions to prevent inpatient falls should be prioritised in medical specialities with high fall rates.

Analysis of the effect of Dividend policy, Debt Policy on Firm Value with Profitabilits as an Innterveneing Variable in Manufacturing Companies Listed on the Indonesia Stock Exchange for the period 2018-2022

The study is entitled “The Effect of Dividend Policy, Debt Policy on Firm Value with Profitability as an Intervening Variable in Manufacturing Companies listed on the Indonesian Stock Exchange 2018-2022 Period”. With the aim of knowing the results of the analysis of the effect of dividend policy (DPR) on firm value (PBV), to find out the results of the analysis of the effect of debt policy (DER) on firm value (PBV), to find out the results of the analysis of the effect of profitability (ROA) on firm value (PBV). ), to find out the results of the analysis of the effect of dividend policy (DPR) on profitability (ROA), to find out the results of the analysis of the effect of debt policy (DER) on profitability (ROA) in Manufacturing Companies listed on the Indonesia Stock Exchange in 2018-2022. And to prove the results of the analysis of the influence of dividend policy (DPR), debt policy (DER) on company value (PBV) with profitability (ROA) as an intervening variable. Sample selection used a purposive sampling technique using certain criteria so that 46 samples were obtained that met the criteria. Data analysis uses multiple linear regression and path analysis. The research results show that dividend policy influences company value, debt policy influences company value, profitability influences company value, dividend policy influences profitability, debt policy influences profitability and profitability is able to mediate the relationship between dividend policy and debt policy on company value.

Content Automation in Marketing Research: A Bibliometric Analysis using VOSviewer

Objectives: This study conducted a comprehensive assessment of content automation in marketing research using bibliometric analysis spanning 20 years from 2004 to 2024. The primary influential factors, topics, and areas of research were analyzed to determine future research paths. Methods/Analysis: Sample selection and data collection were conducted using the Scopus database. The initial dataset was adjusted by applying certain inclusion and exclusion criteria. The final dataset consisting of 149 articles in the RIS format was loaded into the VOSviewer program to perform a bibliometric analysis. Findings: The results revealed key findings, including the highest citation-counting papers, the most common research format, the research field, the year of publication, collaboration countries, the journal, prominent authors, popular themes, areas for further investigation, and the intellectual framework of current research on the topic. Novelty/ Improvement:Four areas were identified for future research: marketing automation as digital commerce content marketing, artificial intelligence for digital marketing transformation, automation and optimization for personalized advertising content, and automatic knowledge discovery. The temporal development of these issues was also examined to offer valuable insights into the changing focus of academic interest over time and establish a foundation for future investigations. Doi: 10.28991/HIJ-2024-05-03-019 Full Text: PDF

Peran Leverage Sebagai Pemediasi Pengaruh Komite Audit dan Sales Growth Terhadap Tax Avoidance

Abstract The purpose of this study is to examine the effect of the audit committee and sales growth on tax avoidance, with leverage as an intervening variable. The population in this study consists of manufacturing industries in the food and beverage sub-sector listed on the Indonesia Stock Exchange (IDX) during the years 2020-2023. The sample selection in this study used purposive sampling, resulting in a total of 57 samples from the food and beverage sub-sector manufacturing industries. The data were analyzed using Partial Least Square (PLS) with the support of the SmartPLS 4 application. The results of the study show that the audit committee has a negative effect on leverage, sales growth has a negative effect on leverage, leverage has a positive effect on tax avoidance, the audit committee has a positive effect on tax avoidance, sales growth has a positive effect on tax avoidance, leverage can mediate the effect of the audit committee on tax avoidance, and leverage cannot mediate the effect of sales growth on tax avoidance. Keywords: Audit committee; sales growth; tax avoidance; leverage.

Recent advances in portable devices for environmental monitoring applications.

Environmental pollution remains a major societal problem, leading to serious impacts on living organisms including humans. Human activities such as civilization, urbanization, and industrialization are major causes of pollution. Imposing stricter rules helps control environmental pollutant levels, creating a need for reliable pollutant monitoring in air, water, and soil. The application of traditional analytical techniques is limited in low-resource areas because they are sophisticated, expensive, and bulky. With the development of biosensors and microfluidics technology, environmental monitoring has significantly improved the analysis time, low cost, portability, and ease of use. This review discusses the fundamentals of portable devices, including microfluidics and biosensors, for environmental control. Recently, publications reviewing microfluidics and biosensor device applications have increased more than tenfold, showing the potential of emerging novel approaches for environmental monitoring. Strategies for enzyme-, immunoassay-, and molecular-based analyte sensing are discussed based on their mechanisms and applications. Microfluidic and biosensor platforms for detecting major pollutants, including metal ions, pathogens, pesticides, and antibiotic residues, are reviewed based on their working principles, advantages, and disadvantages. Challenges and future trends for the device design and fabrication process to improve performance are discussed. Miniaturization, low cost, selectivity, sensitivity, high automation, and savings in samples and reagents make the devices ideal alternatives for in-field detection, especially in low-resource areas. However, their operation with complicated environmental samples requires further research to improve the specificity and sensitivity. Although there is a wide range of devices available for environmental applications, their implementation in real-world situations is limited. This study provides insights into existing issues that can be used as references and a comparative analysis for future studies and applications.

Substrate orientation influence on nanotwinning in magnetron sputtered CoCrFeMnNi and Ni coatings

This study reveals the influence of crystal orientation on formation of growth twins in magnetron-sputtered coatings. A comparison between materials with low and high stacking fault energy (SFE) was made: CoCrFeMnNi (25 mJ/m2) and Ni (125 mJ/m2). The coatings were grown on a polycrystalline 316L stainless-steel substrate with near-random crystal texture, providing a comprehensive selection of samples on a single substrate. Electron backscatter diffraction was used to identify the film orientation, followed by transmission electron microscopy of selected regions.The presence and density of twins depended on both the material and the growth orientation. For Ni, nanotwins were observed on < 5 % of the substrate grains, on growth directions closest to 〈111〉 . All other film orientations grew epitaxially with a cube-on-cube relationship. For CoCrFeMnNi, nanotwinning was observed on 50 % of the substrate grains, deviating < 35° from 〈111〉 . In the nanotwinned regions, the twin spacing was 10–100 nm for Ni and 2–20 nm for CoCrFeMnNi. The presence of nanotwins increased hardness in both materials. The mechanism behind these differences is discussed, together with other parameters for controlling twin density. Our results show that control of the growth process can be used for nanotwin-engineering in magnetron-sputtered materials with low SFE.

Do Protective Tariffs Influence Firms’ Competitive Stance? Application of a Competitive Leverage Model in The Presence of Simultaneous Equation and Sample Selectivity Bias

ABSTRACT: We propose a Competitive Leverage model, and apply it to analyze the impact of tariffs on the attitudes of domestic import-competing firms, especially regarding their disincentives to maintain a Competitive Stance against foreign competitors in the domestic market. We propose that tariffs are a disincentive to the pursuit and maintenance of production efficiency in firms and industries. When countries implement tariff protection for their domestic firms, the most common excuse sounds reasonable, except that it is also economically flawed. While the short-run impacts of such a measure might seem plausible, its long-term effects appear to be less than favorable, and may, in fact, turn out to be damaging to the economy. The paper applies the competitive leverage concept in a unique way to highlight how tariffs distort the output levels of domestic firms, and gives an empirical analysis of the model, using USA data to verify the predictions of the model. Particularly, we develop a Cournot-competition type Competitive Leverage model, and use it to examine the role played by tariffs as a disincentive to a typical firm’s pursuit and maintenance of production efficiency. The competitive leverage model is formulated and applied to verify the effects of tariff protection from the typical domestic firm’s strategic interaction standpoint. We further apply cross-sectional data to carry out an empirical analysis of the model, to verify how tariffs impact the domestic firm’s output and pricing decisions. Our findings would enable us to contribute to the debate about the need to remove protective tariffs, on the grounds that tariffs do not only enable domestic firms to operate inefficiently, but also tends to distort the output and pricing outcomes that would otherwise have been realized in the free market. The results from the study are used to propose some major policy implications of imposing tariff protection, among which are that there is need for implementation of more deliberately sustained Research and development program on the part of domestic firms in the economy, to enable them withstand vigorous competition in a free-trade world. Also, since protective tariffs significantly increase firms’ profit rates, which in turn enables them to unduly earn and accumulate economic rents that amount to inefficiencies and distortions in free-market prices and quantities outcomes, governments should always resist any pressures from big businesses to impose protective tariffs against competition from international rivals. Yet a clear public policy implication from this paper is that, at the macro level, as the results suggest that a government policy that aims at protecting domestic firms through imposition of tariffs may have an adverse effect on the firms’ competitive stance in the industry, a practical message is for the government to impose only a moderate tariff at the most, if it must, especially as it relates to agricultural products.

Feasibility of a Culture-Based Multicomponent Cognitive Training Program for Older Adults With Dementia.

To understand whether a culture-based multicomponent cognitive training program is feasible and tolerable for persons with dementia (PWD) in day care centers in Taiwan, and to examine the effectiveness of a 16-week program in preventing declines in cognitive function and self-care, as well as the development of depressed mood and behavioral problems. This study was a 4-month cluster randomized controlled trial. PWD (N = 56) who met sample selection criteria were recruited from five day care centers and assigned to the experimental or comparison group. Participants in the experimental group received a series of culture-based multicomponent cognitive training activities via research assistants and day care center caregivers for 16 weeks. Participants in the comparison group received usual daily activities. Time attending activities and degree of participation were used as criteria for determining feasibility and tolerability of the program. Results confirmed the feasibility of culture-based multicomponent cognitive training activities if the activities were guided by caregivers. Results also showed participants in the experimental group had statistically significant improvements in cognitive function. Activities of daily living performance, depressed mood, and behavioral problems showed non-significant improvements. The current study suggests that this program can assist PWD in preventing declines in self-care and the development of behavioral and psychological problems, and improve their cognitive function. Further research with a larger sample size and staff training should be considered to test the effectiveness of this program. [Research in Gerontological Nursing, 17(5), 257-264.].

Intelligent fault diagnosis of rotating machine via Expansive dual-attention fusion Transformer enhanced by semi-supervised learning

The precise identification of faults is an essential component in the effective operation and maintenance of rotating machinery (RM). The increasing adoption of deep learning in this field is likely due to the vast amounts of automatically generated monitoring data and its robust ability to learn and detect errors effectively. Deep learning techniques generally demonstrate high efficacy, particularly when limited fault data is available or when employing a two-stage diagnostic approach, involving manual feature extraction followed by fault classification. To address these challenges, we introduce the Expansive Dual-Attention Fusion Transformer (EDAF-Transformer), which is trained through a semi-supervised approach using minimal labeled data. Our method begins with a detailed explanation of the foundational theories behind transformers and semi-supervised learning strategies. This establishes the basis of our approach to intelligent fault diagnosis, particularly under the constraints of raw vibration signals and limited labeled data availability. We then present our novel model, the EDAF-Transformer , which comprises two main components: the Expansive Dual-Attention Enhanced Modules and the 1-D Twin Global Self-Attention Transformer Encoder Module. The first component employs an Expansive Dual-branch Attention Enhanced Module (EDAEM) within a dual-branch attention architecture. This design effectively enlarges the receptive field, capturing both global and local features. The second component, the Twin Sparse Self-Attention Fusion Module (TSSAFM), integrates Global Sparse Self-Attention with Squeeze-and-Excitation (SE) attention. This module is designed to enhance feature encoding capabilities while simultaneously reducing computational demands. Furthermore, we implement an uncertainty self-training semi-supervised strategy focused on balanced unlabeled sample selection. This approach significantly enhances the generalizability and performance of the EDAF-Transformer . We extensively tested our semi-supervised fault diagnosis method on publicly available motor bearing data and a specially curated transmission shaft dataset. The results from these tests demonstrate that our method surpasses other intelligent fault diagnosis methods in effectiveness.

The Effect of Tax Planning and Leverage on Firm Value with Firm Size as a Moderating Variable

This study aims to examine the influence of tax planning and leverage on firm value using firm size as a moderating variable. The model employed in this study is a descriptive quantitative research design using secondary data. The population in this study includes consumer goods sector companies listed on the Indonesia Stock Exchange from 2018-2023. The sample selection process used a purposive sampling method, resulting in 156 observations from 26 companies selected as samples. Hypothesis testing is conducted using a moderation analysis model. The results of the study indicate that tax planning affects firm value and leverage affects firm value. Firm size as a moderating variable is capable of moderating the influence of tax planning and leverage on firm value.

Detecting relativistic Doppler by multi-tracing a single galaxy population

New data from ongoing galaxy surveys, such as the Euclid satellite and the Dark Energy Spectroscopic Instrument (DESI), are expected to unveil physics on the largest scales of our universe. Dramatically affected by cosmic variance, these scales are of interest to large-scale structure studies as they exhibit relevant corrections due to general relativity (GR) in the n-point statistics of cosmological random fields. We focus on the relativistic, sample-dependent Doppler contribution to the observed clustering of galaxies, whose detection will further confirm the validity of GR in cosmological regimes. Sample- and scale-dependent, the Doppler term is more likely to be detected via cross-correlation measurements, where it acts as an imaginary correction to the power spectrum of fluctuations in galaxy number counts. We present a method allowing us to exploit multi-tracer benefits from a single data set, by subdividing a galaxy population into two sub-samples, according to galaxies’ luminosity/magnitude. To overcome cosmic variance we rely on a multi-tracer approach, and to maximise the detectability of the relativistic Doppler contribution in the data, we optimise sample selection. As a result, we find the optimal split and forecast the relativistic Doppler detection significance for both a DESI-like Bright Galaxy Sample and a Euclid-like Hα galaxy population.

BOARD ATTRIBUTES AND TIMELINESS OF FINANCIAL REPORTS OF LISTED NON-FINANCIAL FIRMS IN NIGERIA

This study is on board attributes and timeliness of financial reports of listed non-financial firms in Nigeria. The study covers a period of ten (10) years from 2011 to 2020. The study embraced the correlational research design. The population of the study comprises of one hundred and fourteen (114) non-financial firms that are listed on the NXG as at 31st December 2020 out of which sixty (60) was selected using a two-point filter to eliminate the firms that has not fulfil the criteria for the sample selection for the study. The dependent variable of the study is timeliness of financial reports and is proxied by audit report lag. The independent variable which is board attributes is proxied by board size and board gender. While the control variable profitability and firm size. Board attribute was found to have a negative and significant impact on timeliness of financial report of listed non-financial firms in Nigeria. This implies that for every increase in the board size and an increase in the number of females on the board, there is a significant reduction in the audit report lag among listed non-financial firms in Nigeria. It can be concluded that board attribute reduces audit report delay among listed non-financial firms in Nigeria. It is therefore recommended that the board of directors of listed non-financial firms should reduce the level of leverage in their capital structure since it was found that delay in audit report increases with an increase in leverage.

CoFix: Advancing Semi-Supervised Learning with Noisy Label Mitigation Through Sample Selection and Consistent Regularization

Deep Neural Networks (DNNs) have revolutionized various fields through their ability to model complex patterns, yet their performance critically hinges on the availability of large-scale, accurately labeled datasets. The costly and time-consuming process of creating such datasets has motivated research into Learning with Noisy Labels (LNL), which aims to reduce reliance on perfect labels. This paper introduces CoFix, an innovative LNL framework that integrates sample selection with semi-supervised learning techniques to address the challenge of noisy labels in training. CoFix employs a Gaussian Mixture Model (GMM) to dynamically segment the training data into clean and noisy subsets, leveraging semi-supervised learning approaches on both. Inspired by the FixMatch algorithm, CoFix refines consistent regularization and pseudo-labeling strategies, enhancing augmentation strategies and temperature sharpening techniques. Additionally, CoFix explores label smoothing to augment the loss function, further refining model performance. Our experiments demonstrate CoFix’s superiority over state-of-the-art methods, achieving significant improvements in fewer training epochs, particularly in lower noise scenarios. The robustness and versatility of CoFix are evident through its consistent performance across various benchmark datasets and noise levels. The contributions of this paper include a novel LNL method with enhanced generalization capability, an investigation into the impact of label smoothing on the loss function, and extensive testing that confirms CoFix’s efficiency and adaptability to different noise levels.