Diverse Methodologies Research Articles

What Drives Social Accounting Research? Insights From a Bibliometric Analysis

ABSTRACTWith the increasing importance of sustainability and corporate social responsibility across the globe, social accounting practices are being accepted worldwide; however, the rate of acceptance varies. By analyzing trends, patterns, and future areas of social accounting research, this study aims to address the gap in systematic analysis in this area of research. In the present study, social accounting literature is reviewed through bibliometric techniques, and the vast body of research associated with social accounting is systematized to identify major trends, the most successful authors, methodological variety, and topic relevance. Bibliographic data are retrieved from Scopus and Web of Science databases. Data analysis is performed using Biblioshiny R and Python programming languages. The corpus includes publications from 1946 to March 2024, and 2286 publications remain in the final sample. The results indicate a 6.2% growth rate per year, and the leading contributing country is the UK, followed by Australia, Italy, and the USA. Methodological diversity analysis shows a commitment to empirical research, in which surveys and quantitative research dominate. The topic analysis identified 10 major topics. The studie's innovation lies in the comprehensive nature of bibliometric analysis, which provides insights into the growth, development, and diversity of social accounting research. The implications are related to the increasing importance of sustainability and environmental, social, and governance performance areas, while future research opportunities include the use of digital technologies to achieve improved transparency and stakeholder engagement.

Blockchain Forensics: A Systematic Literature Review of Techniques, Applications, Challenges, and Future Directions

Blockchain technology has gained significant attention in recent years for its potential to revolutionize various sectors, including finance, supply chain management, and digital forensics. While blockchain’s decentralization enhances security, it complicates the identification and tracking of illegal activities, making it challenging to link blockchain addresses to real-world identities. Also, although immutability protects against tampering, it introduces challenges for forensic investigations as it prevents the modification or deletion of evidence, even if it is fraudulent. Hence, this paper provides a systematic literature review and examination of state-of-the-art studies in blockchain forensics to offer a comprehensive understanding of the topic. This paper provides a comprehensive investigation of the fundamental principles of blockchain forensics, exploring various techniques and applications for conducting digital forensic investigations in blockchain. Based on the selected search strategy, 46 articles (out of 672) were chosen for closer examination. The contributions of these articles were discussed and summarized, highlighting their strengths and limitations. This paper examines the selected papers to identify diverse digital forensic frameworks and methodologies used in blockchain forensics, as well as how blockchain-based forensic solutions have enhanced forensic investigations. In addition, this paper discusses the common applications of blockchain-based forensic frameworks and examines the associated legal and regulatory challenges encountered in conducting a forensic investigation within blockchain systems. Open issues and future research directions of blockchain forensics were also discussed. This paper provides significant value for researchers, digital forensic practitioners, and investigators by providing a comprehensive and up-to-date review of existing research and identifying key challenges and opportunities related to blockchain forensics.

Evaluation of University Ranking System to Propose a Model for Bangladeshi Universities

This study investigates and compares various university ranking systems, particularly considering the unique situation of Bangladesh. With the rise of globalization and emphasis on research in the knowledge-based economy, university rankings have gained significant attention. However, different organizations use diverse criteria and methodologies, leading to discrepancies in ranking positions. This research employed a two-phase approach. The first phase involved a systematic literature review focusing on globally used indicators and methodologies for university rankings. This phase revealed that research-related indicators hold the highest weightage (67.93%) in most ranking systems. Subsequently, the second phase utilized active ranking algorithms to explore the findings from the first phase. The study concludes that research output significantly influences a university's ranking and competitiveness globally. Considering Bangladesh's lower publication volume compared to other nations, adopting a ranking system like QS or THEWUR could be beneficial. This system aligns with the global focus on research output and could incentivize research efforts within Bangladeshi universities.

Educational Policy: Analysis, Action, and Advocacy Across Contexts

This article is the introduction to a special issue of Educational Policy titled, “Educational Policy: Analysis, Action, and Advocacy Across Contexts.” The special issue presents contemporary education policy analyses from the United States, Canada, and Australia, which collectively represent methodological, contextual, and theoretical diversity. Individually, they offer detailed examinations of education policy processes and outcomes with a common focus on equity. The articles represent a spectrum of approaches to analysing education policymaking and enactment and point out various ways that educators, scholars, policymakers, and activists can take action. In this article, the co-editors identify key themes that distinguish the special issue’s contribution and explain the importance of education policy analysis that informs future advocacy toward more equitable policy processes and outcomes across contexts.

Integrative analysis of bulk and single-cell RNA sequencing reveals sphingolipid metabolism and immune landscape in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is characterized by its aggressive behavior and complex molecular heterogeneity, posing significant challenges for treatment and prognostication. This study offers a comprehensive analysis of ccRCC by leveraging both bulk and single-cell RNA sequencing data, with a specific aim to unravel the complexities of sphingolipid metabolism and the intricate dynamics within the tumor microenvironment (TME). By examining ccRCC samples sourced from public databases, our investigation delves deep into the genetic and transcriptomic landscape of this cancer type. Employing advanced analytical techniques, we have identified pivotal patterns in gene expression and cellular heterogeneity, with a special focus on the roles and interactions of various immune cells within the TME. Significantly, our research has unearthed insights into the dynamics of sphingolipid metabolism in ccRCC, shedding light on its potential implications for tumor progression and strategies for immune evasion. A novel aspect of this study is the development of a risk score model designed to enhance prognostic predictions for ccRCC patients, which is currently pending external validation to ascertain its clinical utility. Despite its contributions, the study is mindful of its limitations, including a reliance on observational data from public sources and a primary focus on RNA sequencing data, which may constrain the depth and generalizability of the findings. The study does not encompass critical aspects, such as protein expression, posttranslational modifications, and comprehensive metabolic profiles. Moreover, its retrospective design underscores the necessity for future prospective studies to solidify these preliminary conclusions. Our findings illuminate the intricate interplay between genetic alterations, sphingolipid metabolism, and immune responses in ccRCC. This research not only enhances our understanding of the molecular foundations of ccRCC but also paves the way for the development of targeted therapies and personalized treatment modalities. The study underlines the importance of cautious interpretation of results and champions ongoing research using diverse methodologies to thoroughly comprehend and effectively combat this formidable cancer type.

Frontiers and developments of data augmentation for image: From unlearnable to learnable

Data augmentation is a crucial technique for expanding training datasets, effectively alleviating the overfitting issue that arises from limited training data in deep learning models. This paper takes a fresh perspective and offers a scholarly exploration of image data augmentation, following a logical progression from unlearnable to learnable methods. The paper begins by providing a brief overview of the developmental history of data augmentation. It categorizes data augmentation techniques into unlearnable and learnable based on their “variation” strategies. Furthermore, the paper outlines the fundamental properties of data augmentation, including expansiveness, fidelity, generalizability, and self-adaptability. Subsequently, focusing on unlearnable and learnable data augmentation techniques, the paper further divides them into single-sample and multi-sample, global and local, image domain, and feature domain, categorically reviewing the basic principles and effects of various data augmentation methods based on the differences in the sources, scopes, and content of “variation” attributes. Ultimately, the comparative analysis of diverse data augmentation methodologies in specific tasks is conducted alongside a synthesis and projection of future research directions. By comprehensively analyzing diverse image data augmentation methods from a fresh perspective, this review reveals the intrinsic disparities between unlearnable and learnable data augmentation techniques. It paves the way for scholars to embark on innovative paths in data augmentation.

Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts.

Microstructure and mechanical properties of TiC reinforced NbC-Ni4VC4Mo2C cermets

Abstract The class of NbC-Ni cermets has emerged as a promising environmentally friendly alternative to WC-Co cemented carbide tools, although some inherent properties i.e. room temperature hardness and flexural strength, of NbC-Ni cermets may require further enhancements. To enhance the properties of NbC-Ni cermets, diverse methodologies were applied, including grain inhibition and the strengthening of the cermet through the use of reinforcements. This study systematically examined the impact of TiC additions, specifically at 10% and 20%, on both the microstructure and mechanical properties of NbC-12Ni-4VC-4Mo2C cermets. NbC-12Ni cermets were fabricated via vacuum liquid phase sintering (LPS) at 1400 °C and 1450 °C. Enhancements by incorporating 4% VC and 4% Mo2C, followed by TiC at 10% and 20% concentrations to the NbC-Ni-4VC4Mo2C cermet were made. XRD and SEM results confirm the formation of (Nb, X, Y) C solid solution cubic FCC carbides. Significant grain refinement was evident in the cermet samples, subjected to grain inhibition. The observed grain sizes ranged from a maximum of 6.84 μm (in NbC12Ni) to a minimum of 2.154 μm (in the sample containing 20% TiC) at 1450 °C. The incorporation of 20TiC at 1450 °C led to a remarkable 26.5% enhancement in average hardness compared to NbC-Ni cermet, and a 4.2% improvement over the 4VC4Mo2C cermet. At 1450 °C, 20TiC reinforcement led to an average fracture toughness of 9.331 MPa√m, representing a 4.56% improvement over the 4VC4Mo2C cermet. However, there was a marginal reduction in toughness compared to the NbC-Ni cermet. Moreover, the addition of TiC led to a decrease in the flexural strength of the cermets, with a maximum flexural strength of 939 N mm−2 recorded for NbC-Ni-4VC4Mo2C, representing an 11.6% increase compared to the NbC-Ni cermet.

Evaluating performance: a scoping review on video-based assessments of non-technical skills in the operating room

Background: Patient safety remains a critical concern in the high-stakes environment of the operating room (OR). Human factors and non-technical skills (NTS) play pivotal roles in surgical performance and in preventing errors. Various assessment tools and methodologies have been developed to evaluate NTS among OR personnel, reflecting the growing recognition of their importance. Objectives: To map and analyze NTS among OR personnel during real-life surgeries using medical video recording systems and to evaluate NTS assessment tools described in existing literature. Methods: Four databases, PubMed, Web of Science, Medline and Embase, were searched for relevant studies. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol was followed. Eligibility criteria included studies focusing on video recordings providing an overview of the entire OR with a focus on NTS. Results: Twenty-four articles were included in this scoping review. A total of eight NTS were assessed, most frequently communication (79%), leadership (54%), teamwork (50%) and situational awareness (50%). Observational methods varied, ranging from one or more cameras to more sophisticated medical data recorders, such as the OR Black Box (ORBB). Additionally, a wide variety of assessment methods were used, mostly validated tools (NOTSS, SPLINTS, ANTS), but also broader systems-based frameworks (SEIPS) or self-developed tools. Conclusions: This scoping review highlights the diverse methodologies used to measure NTS in the OR using medical video recordings, indicating a need for further research to address challenges and standardize practices. Despite the proven potential benefits of video recording in the OR, legal, ethical and logistical challenges may serve as possible barriers to actual implementation.

Polynomial kernel learning for interpolation kernel machines with application to graph classification

Since all training data is interpolated, interpolating classifiers have zero training error. However, recent work provides compelling reasons to investigate these classifiers, including their significance for ensemble methods. Interpolation kernel machines, which belong to the class of interpolating classifiers, are capable of good generalization and have proven to be an effective substitute for support vector machines, particularly for graph classification. In this work, we further enhance their performance by studying multiple kernel learning. To this end, we propose a general scheme of polynomial combined kernel functions, employing both quadratic and cubic kernel combinations in our experimental work. Our findings demonstrate that this approach improves performance compared to individual graph kernels. Our work supports the use of interpolation kernel machines as an alternative to support vector machines, thereby contributing to greater methodological diversity.

Revolutionizing health monitoring: Integrating transformer models with multi-head attention for precise human activity recognition using wearable devices.

A daily activity routine is vital for overall health and well-being, supporting physical and mental fitness. Consistent physical activity is linked to a multitude of benefits for the body, mind, and emotions, playing a key role in raising a healthy lifestyle. The use of wearable devices has become essential in the realm of health and fitness, facilitating the monitoring of daily activities. While convolutional neural networks (CNN) have proven effective, challenges remain in quickly adapting to a variety of activities. This study aimed to develop a model for precise recognition of human activities to revolutionize health monitoring by integrating transformer models with multi-head attention for precise human activity recognition using wearable devices. The Human Activity Recognition (HAR) algorithm uses deep learning to classify human activities using spectrogram data. It uses a pretrained convolution neural network (CNN) with a MobileNetV2 model to extract features, a dense residual transformer network (DRTN), and a multi-head multi-level attention architecture (MH-MLA) to capture time-related patterns. The model then blends information from both layers through an adaptive attention mechanism and uses a SoftMax function to provide classification probabilities for various human activities. The integrated approach, combining pretrained CNN with transformer models to create a thorough and effective system for recognizing human activities from spectrogram data, outperformed these methods in various datasets - HARTH, KU-HAR, and HuGaDB produced accuracies of 92.81%, 97.98%, and 95.32%, respectively. This suggests that the integration of diverse methodologies yields good results in capturing nuanced human activities across different activities. The comparison analysis showed that the integrated system consistently performs better for dynamic human activity recognition datasets. In conclusion, maintaining a routine of daily activities is crucial for overall health and well-being. Regular physical activity contributes substantially to a healthy lifestyle, benefiting both the body and the mind. The integration of wearable devices has simplified the monitoring of daily routines. This research introduces an innovative approach to human activity recognition, combining the CNN model with a dense residual transformer network (DRTN) with multi-head multi-level attention (MH-MLA) within the transformer architecture to enhance its capability.

The Growth of Mobile Learning Applications in Language Education: A Bibliometric Overview

The rapid advancement of mobile technology has transformed education, with mobile learning applications (m-learning) becoming integral to language education. This study provides a bibliometric analysis of research on mobile learning applications in language education, examining trends, types of research, and key themes from 2010 to 2023. Data were collected from major academic databases, including Scopus, Web of Science, SINTA, and Google Scholar, using keywords related to mobile learning and language education. The findings reveal a significant increase in publications over the past five years, reflecting the growing academic interest in m-learning. Qualitative research methods have been predominantly used, focusing on user experiences and the contextual nature of mobile learning. Critical thinking skills have emerged as a key area of focus, indicating a shift towards using mobile learning for deeper cognitive processes. The study also highlights the central role of students as research subjects, while noting a gap in research on other stakeholders, such as teachers and administrators. Data collection has primarily relied on tests to measure cognitive outcomes, but the study suggests incorporating a variety of methods to capture a more holistic view of the learning experience. The conclusion emphasizes the need for ongoing research to optimize the use of mobile learning applications in language education, advocating for a broader inclusion of stakeholders and diverse research methodologies. This bibliometric overview contributes to the understanding of mobile learning's impact on language education and offers insights for future research directions.

Quality Optimizing Teaching Decisions in Flipped Classrooms Through Data-Driven Strategies

In the context of digitalized educational research, the rapid advancement of internet technologies and big data has catalyzed the transformation of traditional teaching models. The flipped classroom, recognized for its flexibility and efficiency, has garnered significant attention. However, the challenge of scientifically optimizing teaching decisions in flipped classrooms to maximize educational outcomes remains critical. Previous studies have achieved some progress in optimizing teaching decisions within flipped classrooms, yet they often suffer from a lack of methodological diversity, inadequate consideration of multi-level constraints, and struggle to adapt to dynamic teaching environments. Addressing these deficiencies, this research introduces a hybrid evolutionary algorithm combining differential evolution and greedy backtracking. Defined and classified constraints within flipped classroom teaching decisions, the construction of constraint networks, and the creation of multi-level decision spaces are transformed and solved through this hybrid algorithm, offering a systematic optimization strategy. Case analysis confirms the effectiveness and practicality of the proposed method, aiming to bolster decision-making in flipped classrooms and advance the development of digitalized teaching.

Recent Advances in Research from Nanoparticle to Nano-Assembly: A Review.

The careful arrangement of nanomaterials (NMs) holds promise for revolutionizing various fields, from electronics and biosensing to medicine and optics. This review delves into the intricacies of nano-assembly (NA) techniques, focusing on oriented-assembly methodologies and stimuli-dependent approaches. The introduction provides a comprehensive overview of the significance and potential applications of NA, setting the stage for review. The oriented-assembly section elucidates methodologies for the precise alignment and organization of NMs, crucial for achieving desired functionalities. The subsequent section delves into stimuli-dependent techniques, categorizing them into chemical and physical stimuli-based approaches. Chemical stimuli-based self-assembly methods, including solvent, acid-base, biomolecule, metal ion, and gas-induced assembly, are discussed in detail by presenting examples. Additionally, physical stimuli such as light, magnetic fields, electric fields, and temperature are examined for their role in driving self-assembly processes. Looking ahead, the review outlines futuristic scopes and perspectives in NA, highlighting emerging trends and potential breakthroughs. Finally, concluding remarks summarize key findings and underscore the significance of NA in shaping future technologies. This comprehensive review serves as a valuable resource for researchers and practitioners, offering insights into the diverse methodologies and potential applications of NA in interdisciplinary research fields.

Green horizons: Synergized green technological innovation and green energy consumption on ecological degradation

The ecological environment is at great risk due to economic activities owing to investments, rapid industrialization, and energy resources depletion, which pose significant environmental challenges globally. Recent research has emphasized on the crucial role of green energy consumption (GEN) and green technology innovation in attaining environmental sustainability. However, there are still areas that need to be addressed. This research study investigates the role of GEN, green technology innovation, and foreign direct investment while controlling the effect of economic growth (EGR) and trade openness level on the ecological footprint (EFP) using data from 1999 to 2022 for Pakistan. We used diverse econometric methodologies to ensure the data consistency. We examine the long-term relationships among the study variables using fully modified ordinary least-squares and canonical co-integrating regression. The results revealed that green technology innovation, and GEN emerge as pivotal in reducing environmental degradation. Conversely, foreign direct investment and EGR increased the EFP, validating the pollution haven hypothesis in Pakistan. These findings highlighted the significance of green technology innovation that increased GEN to address ecological degradation. These findings contribute to more extensive discussions on environmental sustainability and development of the green horizon, particularly in the context of developing countries like Pakistan.

Exploration of Eye-Tracking Methodologies in Usability Testing of Digital Health Technology: A Rapid Review.

Eye-tracking is deemed a promising methodology for usability evaluation studies in healthcare, however clear theoretical guidance and practice remains lacking. A rapid review was performed on current use of eye tracking as a usability evaluation method on digital health technologies in the period of 2019 to 2024. Usability evaluation studies were included when they described a digital health technology intervention in which eye-tracking technologies were applied. To gain insight into how eye-tracking technologies contributed to measuring digital health technologies' usability, data was extracted on the use of eye-tracking for usability and key study findings. Seventeen papers were included in the review. Findings show that eye-tracking is frequently combined with other usability evaluation methods, with high methodological diversity, to test the usability of DHT. Future research is needed to enhance understanding of the effectiveness of eye-tracking outcomes in DHT usability testing when combined with other usability evaluation methods in order to provide (usability) researchers theoretical guidance on its application.

Editors' Introduction

This issue of the Interdisciplinary Journal of Signage and Wayfinding underscores the significance of involving end users, including the public, in the design and policy development of signage systems. The issue explores the multifaceted roles of signs and symbols in reflecting community character, supporting business branding, aiding wayfinding, and enhancing placemaking. Emphasizing evidence-based design and research, the articles present diverse methodologies, including stakeholder theory, qualitative studies, and shadowing techniques, to inform signage practices.

The Views of Medical Students On The Educational Processes In Pre-Clinical Period

The aim of the research is to reveal the opinions of medical students about the education process in the pre-clinical period in order to determine their educational needs. For this purpose, the problem of the research is; “What are the thoughts of medical faculty students about the education and training process before the clinical period?” It is defined as. A qualitative research design was adopted in the research and the case study method was used. For this purpose, third-year students studying at XXX University Faculty of Medicine in 2022-2023 constitute the sample of the research. Convenient sampling method was used to determine the students participating in the research. In addition to having experience in pre-clinical medical education; In-depth interviews were held with students who attended the courses determined by the "Circulatory and Respiratory Systems" committee, which provides the most methodological diversity possible in educational activities such as educational presentations, panels, laboratories, basic medical practices. In in-depth interviews, semi-structured interview technique was used to reveal the medical students’ views on their education processes. Descriptive analysis, one of the qualitative data analysis types, was used in the research. Descriptive analysis was carried out in four stages; creating a framework for descriptive analysis, processing of data according to the thematic framework, description of findings and interpretation of findings. The findings that students thought would have a positive impact on their professional lives were basic medical practices and case-by-case discussions. Non-standardized basic medical practices, limitations in measurement and evaluation, and inadequate mentoring were expressed by students as negative educational activities.

Challenges in institutionalizing evidence-informed priority setting for health service packages: a qualitative document and interview analysis from Iran

BackgroundSetting and implementing evidence-informed health service packages (HSPs) is crucial for improving health and demonstrating the effective use of evidence in real-world settings. Despite extensive training for large groups on evidence generation and utilization and establishing structures such as evidence-generation entities in many countries, the institutionalization of setting and implementing evidence-informed HSPs remains unachieved. This study aims to review the actions taken to set the HSP in Iran and to identify the challenges of institutionalizing the evidence-informed priority-setting process.MethodsRelevant documents were obtained through website search, Google queries, expert consultations and library manual search. Subsequently, we conducted nine qualitative semi-structured interviews with stakeholders. The participants were purposively sampled to represent diverse backgrounds relevant to health policymaking and financing. These interviews were meticulously audio-recorded, transcribed and reviewed. We employed the framework analysis approach, guided by the Kuchenmüller et al. framework, to interpret data.ResultsEfforts to incorporate evidence-informed process in setting HSP in Iran began in the 1970s in the pilot project of primary health care. These initiatives continued through the Health Transformation Plan in 2015 and targeted disease-specific efforts in 2019 in recent years. However, full institutionalization remains a challenge. The principal challenges encompass legal gaps, methodological diversity, fragile partnerships, leadership changeovers, inadequate financial backing of HSP and the dearth of an accountability culture. These factors impede the seamless integration and enduring sustainability of evidence-informed practices, hindering collaborative decision-making and optimal resource allocation.ConclusionsTechnical aspects of using evidence for policymaking alone will not ensure sustainability unless it achieves the necessary requirements for institutionalization. While addressing all challenges is crucial, the primary focus should be on required transparency and accountability, public participation with an intersectionality lens and making this process resilience to shocks. It is imperative to establish a robust legal framework and a strong and sustainable political commitment to embrace and drive change, ensuring sustainable progress.

Pseudomonas fluorescens in plant growth promotion and biocontrol: A focus on secondary metabolites, IAA, and siderophores

Pseudomonas fluorescens, a Gram-negative bacterium abundant in soil, plays a critical role in promoting plant growth and controlling pathogens, demonstrating remarkable biocontrol capabilities. This review explores the utility of P. fluorescens and its ability to produce secondary metabolites, IAA (Indole-3-acetic acid), and siderophores, addressing agricultural challenges under the strains of climate change. It emphasizes its role as a plant growth-promoting bacterium (PGPB), synthesizing recent findings on its contributions to enhancing plant resilience, pathogen resistance, and sustainable agricultural practices. The production of secondary metabolites, IAA, and siderophores by P. fluorescens is examined for its effectiveness in biocontrol, nutrient mobilization, and hormonal regulation. These functions are critically analyzed through diverse research methodologies, including laboratory and field trials, underscoring the bacterium’s pivotal role in advancing agricultural sustainability and productivity. As the agricultural sector increasingly focuses on bio-products and the exploration of soil microorganisms, P. fluorescens emerges as a promising solution to enhance farming resilience in the face of climatic adversities.