Research Outcomes Research Articles

A structured literature review of empirical research on mandatory auditor rotation

This study reviews 128 empirical studies on mandatory auditor rotation (MAR) in light of the long-standing debate on the effectiveness of MAR and the different regulatory choices made worldwide over time. A structured literature review was conducted to address three research questions. How has empirical research on MAR developed from 2000 to 2022? What is the focus and critique of the empirical research on MAR? What is the future of MAR empirical research? The findings reveal growing empirical evidence on MAR. However, this evidence is fragmented and country-based. Scholars have primarily focused on audit firm rotation rather than audit partner rotation. While quantitative research based on archival data predominates, survey questionnaires prevail in a few regions. Evidence on how MAR impacts auditor independence, the auditor–client relationship, and audit market concentration is scant, whereas audit quality is the most investigated research question. Nonetheless, assessing whether MAR benefits audit quality remains challenging because the research outcomes depend on the research question, rotation type, research method, and regulations enforced in the empirical setting. Based on this comprehensive and rigorous overview of worldwide MAR empirical research, knowledge gaps are underlined, avenues for future research are suggested, and warnings are issued to shareholders and policymakers.

NAGase sensing in 3% milk: FET-based specific and label-free sensing in ultra-small samples of high ionic strength and high concentration of non-specific proteins

Biosensing with biological field-effect transistors (bioFETs) is a promising technology toward specific, label-free, and multiplexed sensing in ultra-small samples. The current study employs the field-effect meta-nano-channel biosensor (MNC biosensor) for the detection of the enzyme N-acetyl-beta-D-glucosaminidase (NAGase), a biomarker for milk cow infections. The measurements are performed in a 0.5 μL drops of 3% commercial milk spiked with NAGase concentrations in the range of 30.3 aM–3.03 μM (Note that there is no background NAGase concentration in commercial milk). Specific and label-free sensing of NAGase is demonstrated with a limit-of-detection of 30.3 aM, a dynamic range of 11 orders of magnitude and with excellent linearity and sensitivity. Additional two important research outcomes are reported. First, the ionic strength of the examined milk is ∼120 mM which implies a bulk Debye screening length <1 nm. Conventionally, a 1 nm Debye length excludes the possibility of sensing with a recognition layer composed of surface bound anti-NAGase antibodies with a size of ∼10 nm. This apparent contradiction is removed considering the ample literature reporting antibody adsorption in a predominantly surface tilted configuration (side-on, flat-on, etc.). Secondly, milk contains a non-specific background protein concentration of 33 mg/ml, in addition to considerable amounts of micron-size heterogeneous fat structures. The reported sensing was performed without the customarily exercised surface blocking and without washing of the non-specific signal. This suggests that the role of non-specific adsorption to the BioFET sensing signal needs to be further evaluated. Control measurements are reported.

Is anyone listening? Evaluating the impact of decision systems and analytics research

ABSTRACT The decision support systems (DSS) area is an integral part of the Information Systems discipline. DSS’s main objective is to contribute to the common good by facilitating access to relevant information for improved decision-making. IFIP Working Group 8.3 Decision Support was established in 1981 as a community passionate about creating a body of knowledge on how to support better decision-making. The advances in addressing this mission were regularly presented at the bi-annual meetings of the IFIP WG8.3. Our aim in this paper is to analyse the impact of the research of the group through these conference proceedings. We use a ‘test of time’ as a starting point to identify the sample of papers that have been most noted and cited. Further analysis helps to clarify other parameters that define the impact of this research and allow us to propose strategies to inform more impactful research outcomes in the future.

Experimental study on wavy fin desiccant coated heat exchanger for hybrid air conditioning systems

The desiccant-coated heat exchanger (DCHE) is an energy-efficient alternative dehumidification equipment for humidity control in air conditioning. It exhibits a higher dehumidification performance than other solid desiccant systems and can simultaneously handle both latent and sensible loads. Taking the clue of research outcomes from the alternative heat exchangers to enhance the performance, a wavy fin DCHE is designed, fabricated and tested. Objectives of the study are to (i) analyse the dynamic performance of DCHE, (ii) compare the heat and mass transfer characteristics of plain and wavy fin-type DCHEs, and (iii) examine the influence of different operating parameters on the performance. The experimental investigation shows that the dehumidification capacity and thermal coefficient of performance of the proposed wavy fin DCHE are 0.34 g/s and 1.041, respectively, which are 75% and 60% higher than the conventional plain fin type. In addition, the developed DCHE has an average cooling capacity of 0.85 kW, which is 42% higher. Besides, the total quantity of moisture adsorbed also increased by 35%. Furthermore, the sensitivity analysis of the parametric study identified air velocity, cold water temperature, and inlet humidity ratio as the most influential operating parameters on the performance of wavy fin DCHE.

Trends in research grant applications and outcomes among medical students in the United Kingdom: a national self-reported cross-sectional survey.

Background: Research funding disparities contribute to clinical-academic workforce inequalities1-3. Hence, our study explores the association between student demographics and research grant application rates and outcomes among UK medical students. Methods: This is a national multicentre cross-sectional survey of UK medical students in the 2020-21 academic year. Multiple zero-inflated negative binomial regression and generalized linear model (binomial distribution; logit link) were utilized to investigate the association between student demographics, number of grant applications submitted, and successful grant applications (yes or no). P-values less than a Bonferroni-corrected significance level of 0.05/36 = 0.0014 were statistically significant. Results: 1528 students participated from 36 medical schools. 151 respondents (9.9%) had applied for research grants. Black students submitted applications 2.90 times more often than white students [Incident rate ratio (IRR): 2.90, 95% confidence interval (CI): 1.37-6.16], with no ethnic disparity in the odds of successful applications. Gender did not influence application rates significantly (P = .248), but women were 4.61 times more likely to secure a grant than men [odds ratio: 4.61, 95% CI: 2.04-10.4]. Being a PubMed-indexed author was associated with increased grant application submission rates [IRR: 3.61, 95% CI: 2.20- 5.92] while conducting more research was associated with greater odds of securing a grant [odds ratio: 1.42, 95% CI: 1.17- 1.73]. Conclusion: Although black students submitted more applications, ethnicity did not influence success rates. Gender did not influence application rates, but women were more successful. These findings underscore the need for strategies supporting women and underrepresented students for continued academic achievement after graduation. This abstract has been published as a paper4

Industrial technology network security measurement in international trade under discrete hopfield neural network

As global economic integration continues to advance, international trade has become increasingly vital for the economic development and growth of nations. This research aims to assess the trends in industrial technology security within China’s international trade and provide practical guidance for policy-making, corporate strategies, and international cooperation. The significance of the rising trend in security within China’s international trade industry lies in its establishment of a robust foundation for the long-term development of China’s international trade, contributing to its cooperation and competitiveness with other countries. In addressing the limitations of traditional measurement methods and providing a more comprehensive and accurate assessment of industrial technology security, this research presents an approach based on a discrete Hopfield Neural Network (HNN) for evaluating industrial technology security in international trade. This method integrates multiple indicators, including technology gap rates, to construct the Superior Quality Engineering (SQE) comprehensive evaluation model. The research employs a combination model of “entropy-grey relational-Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)-discrete HNN” to assess industrial technology security. This research evaluates international trade industry technology security using patent data from 2015 to 2022 as samples. The results indicate an overall upward trend in security in China’s international trade industry. Within this trend, the research observes a stepwise increase in scale components, leading to continuous improvement in security. In terms of quality components, although security develops relatively slowly overall, it exhibits a trend of initial gradual decline followed by rapid growth. Regarding efficiency components, there is overall slow growth with periodic fluctuations. This research outcome provides substantial support for the research of industrial technology in international trade. The proposed method can assist businesses in evaluating their technological security in international trade and offer robust support for international trade decision-making.

Machine learning models for abstract screening task - A systematic literature review application for health economics and outcome research

ObjectiveSystematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening.MethodsThis study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and MeSH terms.Results and conclusionsThe HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.

Epoxy resin bioactive dental implant capped with hydroxyapatite and curcumin nanoparticles: a novel approach

Epoxy resin bioactive dental implant capped with hydroxyapatite and curcumin nanoparticles: a novel approach

The hierarchical predictive coding framework of post-traumatic stress disorder

Humans rely on internal models from sensory information to predict future action, and the default mode network (DMN) plays a pivotal role in this process. These models, however, can become distorted or disrupted in cases of post-traumatic stress disorder (PTSD) because traumatic stress can maladaptively alter how humans understand themselves and predict what happens in the world around them. Here, we examine the relationship between PTSD and this predictive phenomenon known as hierarchical predictive coding implicated in the DMN. We postulate that a hierarchical predictive coding framework will help researchers and clinicians more effectively explain the neural and psychological effects of PTSD among individuals who live with this condition, which may contribute to more targeted research and treatment outcomes.

Occupational Therapy in Psychiatric Short-Term Hospitalization Units: Scoping Review.

Given the context, the overarching aim is to identify scientific publications on occupational therapy in Psychiatric-Short-Term-Hospitalization-Units. Specific objectives include: (a) Analyzing the historical development of this research area; (b) Synthesizing existing evidence on the nature of documentary sources on occupational therapy in in Psychiatric-Short-Term-Hospitalization-Units; (c) Detailing the volume of scientific literature on occupational therapy in these units; (e) Evaluating available evidence on occupational therapy interventions to improve functionality, quality of life, and recovery in individuals admitted to in Psychiatric-Short-Term-Hospitalization-Units. A scoping review method was employed to conduct a historical mapping of research on in Psychiatric-Short-Term-Hospitalization-Units. The review proceeded in five stages following PRISMA guidelines. After applying selection criteria, the search identified 446 references. Findings are presented under three headings: (a) Historical trends in the scientific literature on occupational therapy and in Psychiatric-Short-Term-Hospitalization-Units; (b) Nature and volume of articles included in the occupational therapy synthesis in Psychiatric Short-Term Hospitalization Units; (c) Data extraction on methodological variables in the research of occupational therapy articles in in Psychiatric-Short-Term-Hospitalization-Units; and (d) Data extraction on research outcome variables of occupational therapy articles in Psychiatric Short-Term Hospitalization Units. The growth of occupational therapy in in Psychiatric-Short-Term-Hospitalization-Units is emphasized, with an increase in qualitative studies. Occupational therapy is underscored as an integral part of care, supporting the diversity and effectiveness of interventions. Common diagnoses include schizophrenia and depressive disorders. Group interventions and the spiritual dimension positively influence the quality of care and meaningful routines for recovery in in Psychiatric-Short-Term-Hospitalization-Units.

Effect of enzymatic hydrolysis with Alcalase or Protamex on technological and antioxidant properties of whey protein hydrolysates

The aim of this study was to evaluate the effect of the enzymatic hydrolysis, performed using Alcalase and Protamex enzymes, on the technological functionalities and the antioxidant capacity of whey protein hydrolysates (WPHs) to identify the conditions allowing to obtain target functionality/ies. Samples were characterized for hydrolysis degree (DH), molecular weight distribution, structural properties, and food-related functionalities. Free sulfhydryl groups and surface hydrophobicity significantly decreased with the increase in DH, regardless of the used enzyme. The foaming and antioxidant properties of Alcalase WPHs were higher as compared to those of WPI, reaching the maximum value at DH = 18–20 %, while higher DH resulted in impaired functionality. Gelling properties were guaranteed when WPI was hydrolysed by Protamex at DH < 15 % while foaming and antioxidant abilities were fostered at 15 < DH < 21 %. These results were well correlated with MW distribution and were rationalized into a road map which represents a useful tool in the selection of proper hydrolysis conditions (time, DH, enzyme type) to obtain WPHs with tailored functionalities. Research outcomes highlighted the possibility to drive protein hydrolysis to optimize the desired functionality/ies.

Bibliometric Analysis of Spanish Doctoral theses on Public Relations (2006-2021)

Objective: The aim of this article is to conduct a comprehensive and detailed analysis of doctoral theses in the field of public relations carried out at public universities in Spain between 2006 and 2021. This analysis seeks to identify and understand the main dimensions researched, the thematic and methodological evolution, and the contribution of these studies to both the professional practice and theoretical framework of public relations. Theoretical Framework: The theoretical framework is built upon the historical evolution of public relations in both the academic and professional spheres. It acknowledges changes in social demands for greater effectiveness and rigor in research outcomes in this field. Previous studies documenting the transition in methods and approaches used in public relations will be reviewed, emphasizing those that have marked milestones in the integration of theory and practice. Additionally, prevailing theoretical models and how they have been challenged and expanded by recent research will be analyzed. Method: The study employs an exploratory and descriptive methodological design. Content analysis was used to dissect and categorize information systematically for analyzing the doctoral theses. Theses were selected through a purposive sampling of documents available in Spanish academic databases, ensuring that these studies encompass various dimensions and approaches within the field of public relations. Variables such as thematic focus, methodologies employed, and the theoretical and practical contributions of each thesis were assessed. Results and Discussion: This bibliometric study reveals a predominance of doctoral theses in public relations in Spain focused on practical applications, demonstrating a primarily instrumental view of the discipline as a marketing tool. There is a marked imbalance towards applied research, with limited interest in theoretical development, suggesting a lack of exploration of public relations as a strategic and executive function. The scarcity of works that integrate public relations into global marketing strategies and their operational focus highlight an opportunity to expand its strategic role. These findings indicate the need to strengthen theoretical and strategic research in the field, which could enrich both the practice and teaching of public relations, strengthening its position as an essential discipline in organizational management. Originality/Value: This article contributes originality to the field of public relations by providing one of the first exhaustive systematizations of doctoral theses in this area in Spain. The review covers a significant fifteen-year period, allowing for observation of trends and changes in academic research in public relations. The value of this study lies in its ability to identify how demands for rigor and empirical relevance have shaped academic research, thus contributing to a better understanding of the field's evolution and offering a solid foundation for future research aimed at effectively integrating theory and practice.

Research on the academic thought of distinguished veteran doctors of traditional Chinese medicine based on Mendelian randomization: a case study of the causation between Sjögren’s syndrome and rheumatoid arthritis

Aim: This study investigated the causation between Sjögren’s syndrome (SS) and rheumatoid arthritis (RA) based on Mendelian randomization (MR), with the purpose of promoting research on the academic thought of distinguished veteran doctors of traditional Chinese medicine (TCM) via bioinformatics with information technology and providing new basis for better inheriting and carrying forward the valuable experience of a National Master of TCM, Zhizheng Lu. Methods: The causation between RA and SS in TCM was inferred from the perspective of coordination between dryness and dampness. For further assessment, MR was performed based on the summary data of genome-wide association studies (GWAS), with genetic loci closely related to SS(instrumental variables (IVs)), RA(research outcome), inverse-variance weighted (IVW) and MR-Egger regression and weighted median (WME) ( main MR methods). Heterogeneity was evaluated by Cochran Q test, pleiotropy by MR-Egger regression, and robustness by the Leave-One-Out method. Results: Except for MR-Egger, all other methods showed a clear statistical significance in the association between SS and RA (P&lt;0.05). Conclusion: This study, based on the academic thought of distinguished TCM veteran doctor and the thought “moistening dryness” of National Master of TCM, Zhizheng Lu, speculated that there may be a potential association between SS and RA. MR results demonstrated the clear genetic causation between SS and RA, and the guiding role of the academic thought of distinguished TCM veteran doctor in clinical practice genetically.

Events Management Practices and Performance of Selected HEIs in National Capital Region: Inputs for Sustainable Business Generating Projects

Events management practices played a crucial role in the success and performance of higher education institutions (HEIs) in implementing sustainable business generating projects. This study examined the inputs related to events management practices and their impact on the performance of selected HEIs in the context of sustainable business generating projects. The research aimed to provide insights into effective strategies and best practices that could enhance the success of these projects within HEIs. The study adopted a quantitative research approach, utilizing surveys to gather data from a sample of selected HEIs. The study identified key success factors and challenges faced by HEIs in managing events and sustaining their business generating initiatives. It also provided practical recommendations and guidelines for HEIs to optimize their events management practices and improve project performance. The research outcomes were valuable for HEI administrators, event managers, and other stakeholders involved in sustainable business generating projects within the higher education sector. By identifying effective strategies and practices, this study aimed to enhance the ability of HEIs to generate sustainable business opportunities through events and contribute to the overall development and reputation of these institutions. In conclusion, this study sought to bridge the gap in knowledge regarding events management practices and their impact on the performance of HEIs in sustainable business generating projects. The findings informed decision-making processes and enabled HEIs to implement effective and sustainable events management strategies, ultimately leading to enhanced project performance and long-term success.

Statistical data transformation in agrarian sciences for variance analysis: a systematic review

In statistical analyses, a common practice for enhancing the validity of variance analysis is the application of data transformation to convert measurements into a different mathematical scale. This technique was first employed in 1898 by Edgeworth and remains relevant in current scientific publications despite the proliferation of more modern and advanced techniques that obviate the need for certain assumptions. Data transformations, when appropriately used, can make the model error terms approximate a normal distribution. It is also possible to use the technique to correct the heterogeneity of variances or to render an additive model, ensuring the validity of the analysis of variances. Given that this technique can be hastily applied, potentially leading to erroneous or invalid results, we conducted a systematic literature review of studies in the field of agrarian sciences that utilized data transformations for the validation of analysis of variances. The aim was to check the transformations employed by the scientific community, the motivation behind their use, and to identify possible errors and inconsistencies in applying the technique in publications. In this study, we identified shortcomings and misconceptions associated with using this method, and we observed incomplete and inadequate utilization of the technique in 94.28% of the analysed sample, resulting in misguided and erroneous conclusions in scientific research outcomes.

Oil Palm Bunch Ripeness Classification and Plantation Verification Platform: Leveraging Deep Learning and Geospatial Analysis and Visualization

Oil palm cultivation thrives as a prominent agricultural endeavor within the southern region of Thailand, where the country ranks third globally in production, following Malaysia and Indonesia. The assessment of oil palm bunch ripeness serves various purposes, notably in determining purchasing prices, pre-harvest evaluations, and evaluating the impacts of disasters or low market prices. Presently, two predominant methods are employed for this assessment, namely human evaluation, and machine learning for ripeness classification. Human assessment, while boasting high accuracy, necessitates the involvement of farmers or experts, resulting in prolonged processing times, especially when dealing with extensive datasets or dispersed fields. Conversely, machine learning, although capable of accurately classifying harvested oil palm bunches, faces limitations concerning its inability to process images of oil palm bunches on trees and the absence of a platform for on-tree ripeness classification. Considering these challenges, this study introduces the development of a classification platform leveraging machine learning (deep learning) in conjunction with geospatial analysis and visualization to ascertain the ripeness of oil palm bunches while they are still on the tree. The research outcomes demonstrate that oil palm bunch ripeness can be accurately and efficiently classified using a mobile device, achieving an impressive accuracy rate of 99.89% with a training dataset comprising 8779 images and a validation accuracy of 96.12% with 1160 images. Furthermore, the proposed platform facilitates the management and processing of spatial data by comparing coordinates derived from images with oil palm plantation data obtained through crowdsourcing and the analysis of cloud or satellite images of oil palm plantations. This comprehensive platform not only provides a robust model for ripeness assessment but also offers potential applications in government management contexts, particularly in scenarios necessitating real-time information on harvesting status and oil palm plantation conditions.

How Indonesia’s Cities Are Grappling with Plastic Waste: An Integrated Approach towards Sustainable Plastic Waste Management

In Indonesia, plastic constitutes the second largest component of municipal solid waste; however, 58% remains uncollected. Most plastic recycling depends on informal sectors, accounting for only 10% of the total, leaving the remainder to potentially harm the environment. This paper analyzes how cities in Indonesia have tackled their plastic waste problems from the perspective of the integrated sustainable waste management framework. This study focuses on plastic waste management (PWM) in three cities: Bandung, Yogyakarta, and Magelang. Data were collected from 41 semi-structured interviews, a plastic waste composition data analysis, and site visits. The research outcomes include a comprehensive analysis of stakeholder roles, plastic waste flow, plastic waste composition data, and various challenges in PWM. This study identifies three main stakeholders in collecting plastic wastes: the Department of Environment as the main local government body, community-based waste banks, and private waste management companies. Most recyclable plastic waste is recovered through waste banks and private collection services. In 2022, the predominant types of plastic waste in Bandung, Yogyakarta, and Magelang City were soft plastics, thick plastics, and hard plastics, respectively. The challenges for the PWM stem from various aspects in political, legal, and institutional; environmental; financial and technical; and socio-cultural spheres. Positive trends towards an integrated system are observed, although full integration has not yet been achieved. The research recommends a multi-stakeholder cooperative approach involving municipal authorities, waste banks, and private collectors, responsive to local conditions and emphasizing sustainability aspects throughout waste management stages for a sustainable resource recycling in developing countries.

Machine learning-based model for predicting arrival time of container ships

Forecasting container ship arrival times is challenging, requiring a thorough analysis for accuracy. This study investigates the effectiveness of machine learning (ML) techniques in maritime transportation. Using a dataset of 581 samples with 8 input variables and 1 output variable (arrival time), ML models are constructed. The Pearson correlation matrix reduces input variables to 7 key factors: freight forwarder, dispatch location, loading and discharge ports, post-discharge location, dispatch day of the week, and dispatch week. The ranking of ML performance for predicting the arrival time of container ships can be arranged in descending order as GB-PSO &gt; XGB &gt; RF &gt; RF-PSO &gt; GB &gt; KNN &gt; SVR. The best ML model, GB-PSO, demonstrates high accuracy in predicting the arrival time of container ships, with R2 = 0.7054, RMSE = 7.4081 days, MAE = 5.1891 days, and MAPE = 0.0993% for the testing dataset. This is a promising research outcome as it seems to be the first time that an approach involving the use of minimal and easily collectible input factors (such as freight forwarder, dispatch time and place, port of loading, post port of discharge, port of discharge) and the combination of a machine learning model has been introduced for predicting the arrival time of container ships.

Competing risks and multivariate outcomes in epidemiological and clinical trial research.

Data analysis methods for the study of treatments or exposures in relation to a clinical outcome in the presence of competing risks have a long history, often with inference targets that are hypothetical, thereby requiring strong assumptions for identifiability with available data. Here data analysis methods are considered that are based on single and higher dimensional marginal hazard rates, quantities that are identifiable under standard independent censoring assumptions. These lead naturally to joint survival function estimators for outcomes of interest, including competing risk outcomes, and provide the basis for addressing a variety of data analysis questions. These methods will be illustrated using simulations and Women's Health Initiative cohort and clinical trial data sets, and additional research needs will be described.

Prediction of the flow resistance in non-prismatic compound channels

ABSTRACT Achieving an accurate estimation of the flow resistance in open channel flows is crucial for resolving several critical engineering difficulties. In instances when there is excessive flow on both banks of a river, it results in the breach of the primary channel, leading to the discharge of water into the adjacent floodplain. The alteration of floodplain geometry occurs as a consequence of agricultural and developmental practises, leading to the emergence of compound channels that exhibit converging, diverging, or skewed characteristics throughout the course of the flow. The efficacy of conventional equations in accurately forecasting flow resistance is limited due to their heavy reliance on empirical approaches. As a result of this phenomenon, there persists a significant need for methodologies that possess both novelty and precision. The objective of this work is to use the support vector machine (SVM) technique for the estimation of the Manning's roughness coefficient in a compound channel with converging floodplains. Statistical indicators are used to validate the constructed models in the experimental investigation, enabling the assessment of their performance and efficacy. The findings indicate a significant correlation between the Manning's roughness coefficient predicted by SVM and both experimental data and prior research outcomes.