Opportunities For Innovative Research Research Articles

A Bibliometric Examination of Studies on Corporate Tax Avoidance Utilizing Vosviewer Software

The field of corporate tax avoidance is dynamic, with new theories, methods, and empirical findings emerging over time. As tax laws evolve and corporate practices adapt, researchers continually seek to refine understanding of the drivers and consequences of tax avoidance. This research endeavors to systematically analyze and demonstrate bibliometric data using VOSViewer, providing a comprehensive guide for first-time users. By employing mapping tools within VOSViewer, the study examines the trends and evolution of corporate tax avoidance over five years (2018–2023). Utilizing qualitative descriptive methodology, the research presents a detailed network visualization of the subject matter. A search of relevant published journals yielded 1000 articles spanning from 2018 to 2023, categorized into five groups based on publication year. Analysis revealed a total of 180 articles focused on corporate tax avoidance during this timeframe. The study concludes that corporate tax avoidance is an interdisciplinary field ripe for exploration, offering numerous opportunities for innovative research. It is anticipated that this study will serve as a valuable resource for researchers seeking to delve into this topic and determine future research directions.

A Contemporary Secure Microservices Discovery Architecture with Service Tags for Smart City Infrastructures

The re-conceptualization of fundamental elements from traditional monolithic structures to the Microservices framework has emerged as a vital vision for the future of IoT systems. This transformation is pivotal in addressing the present and future challenges faced by IoT systems and enhancing their overall operational qualities. The adoption of Microservices in IoT introduces an array of opportunities for innovative research, which collectively contribute to its feasibility and success. The transition from traditional systems to Microservices in IoT brings forth various complex challenges that need to be effectively addressed. Key challenges include ensuring seamless Microservice discovery, efficient API gateway management, scalable distribution services, reliable uniform service discovery, robust containerization, and stringent access control mechanisms. These challenges encompass both technical and security-related aspects, necessitating innovative solutions to overcome them. To overcome these challenges, our research proposes a secure service discovery architecture that integrates security measures at each stage of the discovery process. This methodology employs advanced encryption techniques, authentication protocols, and authorization mechanisms to safeguard Microservices in IoT. A mathematical framework is introduced to formalize and implement these security measures, ensuring a robust and reliable approach to Microservices adoption in the IoT ecosystem. In the experimental phase of our research, we aim to validate the proposed secure service discovery architecture through a series of rigorous tests and simulations. We will assess its performance, scalability, and security efficacy under various real-world scenarios and IoT use cases. Experimental results and performance metrics will be analyzed to provide empirical evidence of the viability and effectiveness of our proposed methodology in addressing Microservices-related challenges in IoT systems.

Automating data analysis in scientific research of future education specialists using the Google Apps Script platform

Abstract. The article focuses on the role and potential of using the Google Apps Script platform as a tool for automating data analysis in scientific research for future specialists in the educational sphere. Thus, the article aims to systematize and summarize information about the possibilities and advantages of using digital tools, particularly the Google Apps Script platform, for automating data analysis in scientific research. The following research methods were employed: analysis and summarization of technological capabilities and practical cases using the Google Apps Script platform based on existing scientific and methodological sources, modelling, and synthesis of obtained data. This contributed to understanding and assessing the potential of the designated tool for use by future specialists in the educational sphere. The possibility of optimizing various stages of scientific research using Google Apps Script was thoroughly analysed, particularly in data filtering and summarization, statistical analysis, creating graphs, and reporting. Considering the prospects of applying the synthetic method in the research process, the role of the specified tool in creating and expanding data for objective analysis is emphasized. The Google Apps Script platform facilitates the effective combination of analytical capabilities and automation, allowing researchers to better synthesize the obtained results and react promptly to the identified trends. Such a comprehensive approach to data processing creates unique opportunities for innovative research in the field of education, making it more accessible and interactive for use by researchers and practitioners. As a result of the study, the significance of digital tools, including the Google Apps Script platform, in preparing future education professionals was highlighted. The advantages and possibilities of this tool for implementing innovative approaches in data analysis in the field of scientific research were pointed out, emphasizing its potential for developing new methodologies and approaches in this field. In the context of the on-going evolution of educational approaches, the Google Apps Script platform serves not only as a data analysis tool but also as a catalyst for innovative thinking. In summary, it is worth noting that Google Apps Script is a powerful tool that provides opportunities for automating complex data processing processes in scientific research, even when employing the synthetic method. The use of this tool opens up possibilities for efficient analysis and processing of large volumes of information, even when it is critical, overcoming this challenge in modern scientific work. Its capabilities enable researchers to focus on essential aspects of research, reducing time spent on routine data processing operations.

Methodological progress note: Machine learning methods in healthcare research.

Methodological progress note: Machine learning methods in healthcare research.

From supply chain risk to system-wide disruptions: research opportunities in forecasting, risk management and product design

PurposeSupply chains must rebuild for resilience to respond to challenges posed by systemwide disruptions. Unlike past disruptions that were narrow in impact and short-term in duration, the Covid pandemic presented a systemic disruption and revealed shortcomings in responses. This study outlines an approach to rebuilding supply chains for resilience, integrating innovation in areas critical to supply chain management.Design/methodology/approachThe study is based on extensive debates among the authors and their peers. The authors focus on three areas deemed fundamental to supply chain resilience: (1) forecasting, the starting point of supply chain planning, (2) the practices of supply chain risk management and (3) product design, the starting point of supply chain design. The authors’ debated and pooled their viewpoints to outline key changes to these areas in response to systemwide disruptions, supported by a narrative literature review of the evolving research, to identify research opportunities.FindingsAll three areas have evolved in response to the changed perspective on supply chain risk instigated by the pandemic and resulting in systemwide disruptions. Forecasting, or prediction generally, is evolving from statistical and time-series methods to human-augmented forecasting supplemented with visual analytics. Risk management has transitioned from enterprise to supply chain risk management to tackling systemic risk. Finally, product design principles have evolved from design-for-manufacturability to design-for-adaptability. All three approaches must work together.Originality/valueThe authors outline the evolution in research directions for forecasting, risk management and product design and present innovative research opportunities for building supply chain resilience against systemwide disruptions.

Blood supply chain network design: a systematic review of literature and implications for future research

PurposeThe purpose of this study is to systematically review the existing literature on the blood supply chain (BSC) from a network design perspective and highlight the research gaps in this area. Moreover, it also aims to pinpoint new research opportunities based on the recent innovative technologies for the BSC network design.Design/methodology/approachThe study gives a comprehensive systematic review of the BSC network design studies until October 2021. This review was carried out in accordance with preferred reporting items for systematic reviews and meta-analyses (PRISMA). In the literature review, a total of 87 studies were analyzed under six main categories as model structure, application model, solution approach, problem type, the parties of the supply chain and innovative technologies.FindingsThe results of the study present the researchers’ tendencies and preferences when designing their BSC network models.Research limitations/implicationsThe study presents a guide for researchers and practitioners on BSC from the point of view of network design and encourages adopting innovative technologies in their BSC network designs.Originality/valueThe study provides a comprehensive systematic review of related studies from the BSC network design perspective and explores research gaps in the collection and distribution processes. Furthermore, it addresses innovative research opportunities by using innovative technologies in the area of BSC network design.

Plasmonic visible-near infrared photothermal activation of olefin metathesis enabling photoresponsive materials.

Light-induced catalysis and thermoplasmonics are promising fields creating many opportunities for innovative research. Recent advances in light-induced olefin metathesis have led to new applications in polymer and material science, but further improvements to reaction scope and efficiency are desired. Herein, we present the activation of latent ruthenium-based olefin metathesis catalysts via the photothermal response of plasmonic gold nanobipyramids. Simple synthetic control over gold nanobipyramid size results in tunable localized surface plasmon resonance bands enabling catalyst initiation with low-energy visible and infrared light. This approach was applied to the ROMP of dicyclopentadiene, affording plasmonic polymer composites with exceptional photoresponsive and mechanical properties. Moreover, this method of catalyst activation was proven to be remarkably more efficient than activation through conventional heating in all the metathesis processes tested. This study paves the way for providing a wide range of photoinduced olefin metathesis processes in particular and photoinduced latent organic reactions in general by direct photothermal activation of thermally latent catalysts.

Dive Into the Unknown: Embracing Uncertainty to Advance Aquatic Remote Sensing

Uncertainty is an inherent aspect of aquatic remote sensing, originating from sources such as sensor noise, atmospheric variability, and human error. Although many studies have advanced the understanding of uncertainty, it is still not incorporated routinely into aquatic remote sensing research. Neglecting uncertainty can lead to misinterpretations of results, missed opportunities for innovative research, and a limited understanding of complex aquatic systems. In this article, we demonstrate how working with uncertainty can advance remote sensing through three examples: validation and match-up analysis, targeted improvement of data products, and decision-making based on information acquired through remote sensing. We advocate for a change of perspective: the uncertainty inherent in aquatic remote sensing should be embraced, rather than viewed as a limitation. Focusing on uncertainty not only leads to more accurate and reliable results but also paves the way for innovation through novel insights, product improvements, and more informed decision-making in the management and preservation of aquatic ecosystems.

The future of work: a systematic literature review

AbstractThis paper provides a systematic review of literature pertaining to the future of work. Since the early 1990s, scholars have been engaged in research to better understand workplace culture change. By conducting a state‐of‐the‐art literature review, we identify the 32 most influential publications in the field that have developed into four distinct categories and we review each of these to identify the main contributions of these research areas. With a highlight on possible pathways for future research, this paper outlines these emerging trends to integrate on existing knowledge and pursue innovative research opportunities to expand the research frontier.

Extrapial Hippocampal Resection in Anterior Temporal Lobectomy: Technical Description and Clinical Outcomes in a 62-Patient Case Series.

Anterior temporal lobectomy (ATL) is the most effective treatment for drug-resistant mesial temporal lobe epilepsy. Extrapial en bloc hippocampal resection facilitates complete removal of the hippocampus. With increasing use of minimally invasive treatments, considering open resection techniques that optimize the integrity of tissue specimens is important both for obtaining the correct histopathological diagnosis and for further study. To describe the operative strategy and clinical outcomes associated with an extrapial approach to hippocampal resection during ATL. A database of epilepsy surgeries performed by a single surgeon between October 2011 and February 2019 was reviewed to identify all patients who underwent ATL using an extrapial approach to hippocampal resection. To reduce confounding variables for outcome analysis, subjects with prior resections, tumors, and cavernous malformations were excluded. Seizure outcomes were classified using the Engel scale. The surgical technique is described and illustrated with intraoperative images. A total of 62 patients met inclusion criteria (31 females) for outcome analysis. Patients with most recent follow-up <3 yr (n=33) and >3 yr (n=29) exhibited 79% and 52% class I outcomes, respectively. An infarct was observed on postoperative magnetic resonance imaging in 3 patients (1 asymptomatic and 2 temporarily symptomatic). An en bloc specimen in which the subiculum and all hippocampal subfields were preserved was obtained in each case. Examples of innovative research opportunities resulting from this approach are presented. Extrapial resection of the hippocampus can be performed safely with seizure freedom and complication rates at least as good as those reported with the use of subpial techniques.

The convergence of big data and accounting: innovative research opportunities

This study aims to develop accounting standards, curriculums, and research to cope with the rapid development of big data. The study presents several potential convergence points between big data and different accounting techniques and theories. The study discusses how big data can overcome the data limitations of six accounting issues: financial reporting, performance measurement, audit evidence, risk management, corporate budgeting and activity-based techniques. It presents six exciting research questions for future research. Then, the study explains the potential convergence between big data and agency theory, stakeholders theory, and legitimacy theory. This theoretical study develops new convergence points between big data and accounting by reviewing the literature and proposing new ideas and research questions. The conclusion indicates a significant convergence between big data and accounting on the premise that data is the heart of accounting. Big data and advanced analytics have the potential to overcome the data limitations of accounting techniques that require estimations and predictions. A remarkable convergence is argued between big data and three accounting theories. Overall, the study presents helpful insights to members of the accounting and auditing community on the potential of big data.

Doxorubicin hydrochloride liposome and albumin-bound paclitaxel in cancer: a nanotechnology perspective

Nanoparticles (1-100 nanometres in size), products of nanotechnology, offer a modern way to transport anti-cancer drugs by acting as transporters of drugs into tumor cells, hence quenching tumor cell proliferation. Such nanoparticles may be formulated to bind to the tumor cell membrane or inhibit specific reactions of tumor biosynthetic pathway by gene repression, or directly bind to the active sites of essential enzymes in the biosynthetic pathway. Consequently, drugs are completely delivered to the desired cancerous cells without system interference. Liposomal doxorubicin and albumin-bound paclitaxel are two examples of nanotechnologically developed drugs for treating cancer. Modern knowledge of nanotechnology opens up new opportunities for innovative research on cancer therapies and administration and helps minimize harm to healthy cells. This review focuses on the doses and routes of administration of these chemotherapeutic agents used in treating cancers.

ARYL HYDROCARBON RECEPTOR ANTAGONISTS - A NEW ENTRY IN ANTIHYPERTENSIVE ARMAMENTARIUM OF OBSTRUCTIVE SLEEP APNEA?

Objective: To investigate aryl hydrocarbon receptor (AhR) metabolic circuitry as a druggable target in chronic intermittent hypoxia (CIH) induced hypertension (HTA). Design and method: This study was approved by Nova Medical School's Ethics Committee and followed national and international regulations. We used a rat model of CIH-induced HTA to disclose the hypothesis that the aryl hydrocarbon receptor (AHR) is activated by IH, once it shares the same binding partner of HIF-1alpha and promotes pro-oxidant, pro-inflammatory (NF-kB) and pro-fibrotic events in common with CIH. AhR activation was measured by the mRNA and protein levels of its hallmark target gene Cyp1a1. Blood pressure, heart rate and locomotor activity were measured daily by radiotelemetry. A CIH paradigm of 21% to 5% of O2, 5.6 cycles/h, 10.5 h/day was used in their inactive/sleep period. Results: Upon established hypertension (21 days exposure to IH), we observed an increase in Cyp1a1 mRNA (hallmark of AhR activation) in kidney cortex (6-fold), kidney medulla (3-fold) and liver (3-fold), but not in other tissues. Increased renal expression of Ahr and markers of inflammation (Rela), epithelial to mesenchymal transition markers, the rate-controlling step of gluconeogenesis, Pepck1, and members of HIF-pathway, namely, Hif-3alpha were also observed. The ratio kynurenin/tryptophan (inflammatory index) was also increased in the kidney. The chronicity of IH increased the protein levels of cyp1a1 protein (western blot). Daily administration (14 days) of AHR antagonist, CH-223191 (5 mg.kg-1.day-1, gavage), simultaneously to CIH prevented the increase in systolic blood pressure (SBP) by 53 ± 12% and in diastolic blood pressure (DBP) by 44 ± 16%. Moreover, this AhR antagonist administration upon already established HTN reversed the increase in SBP by 52 ± 12%. Despite the antihypertensive effect seen in the rats’ active phase, in the inactive period, CH22319 did not revert the non-dipping blood pressure profile associated to CIH. Conclusions: CIH caused an activation of AHR signaling particularly in the kidney and its pharmacological blockade had a significant impact reverting already established HTN. This first evidence inspires innovative research opportunities for the understanding and treatment of this particular type of HTN.

Climate change impacts on wind power generation

Wind energy is a virtually carbon-free and pollution-free electricity source, with global wind resources greatly exceeding electricity demand. Accordingly, the installed capacity of wind turbines grew at an annualized rate of >20% from 2000 to 2019 and is projected to increase by a further 50% by the end of 2023. In this Review, we describe the factors that dictate the wind resource magnitude and variability and illustrate the tools and techniques that are being used to make projections of wind resources and wind turbine operating conditions. Natural variability due to the action of internal climate modes appears to dominate over global-warming-induced non-stationarity over most areas with large wind energy installations or potential. However, there is evidence for increased wind energy resources by the end of the current century in northern Europe and the US Southern Great Plains. New technology trends are changing the sensitivity of wind energy to global climate non-stationarity and, thus, present new challenges and opportunities for innovative research. The evolution of climate modelling to increasingly address mesoscale processes is providing improved projections of both wind resources and wind turbine operating conditions, and will contribute to continued reductions in the levelized cost of energy from wind power generation.

First evidence of aryl hydrocarbon receptor as a druggable target in hypertension induced by chronic intermittent hypoxia

Background and PurposeObstructive sleep apnea (OSA) is associated to a high prevalence of resistant arterial hypertension (HTN) justifying the research on novel targets. Chronic intermittent hypoxia (CIH) is a key feature in the development of OSA comorbidities, including HTN. Experimental ApproachWe used a rat model of CIH-induced HTN to disclose the hypothesis that the aryl hydrocarbon receptor (AHR) is activated by CIH once it shares the same binding partner of HIF-1α and promotes pro-oxidant, pro-inflammatory (NF-kB) and pro-fibrotic events in common with CIH. Key ResultsUpon established hypertension (21 days exposure to CIH), we observed an increase in Cyp1a1 mRNA in kidney cortex (6-fold), kidney medulla (3-fold) and liver (3-fold), but not in other tissues. Increased renal expression of Ahr and markers of inflammation (Rela), epithelial to mesenchymal transition markers, the rate-controlling step of gluconeogenesis, Pepck1, and members of HIF-pathway, namely, Hif3a were also observed. Daily administration (14 days) of AHR antagonist, CH-223191 (5 mg.kg-1.day-1, gavage), simultaneously to CIH prevented the increase in systolic blood pressure (SBP) by 53 ± 12% and in diastolic blood pressure (DBP) by 44 ± 16%. Moreover, its administration (14 days) upon already established HTN reversed the increase in SBP by 52 ± 12%. Conclusion and ImplicationsCIH caused an activation of AHR signaling particularly in the kidney and its pharmacological blockade had a significant impact reverting already established HTN. This first evidence inspires innovative research opportunities for the understanding and treatment of this particular type of HTN.

Transdisciplinary Bioblitz: Rapid biotic and abiotic inventory allows studying environmental changes over 60 years at the Biological Field Station of Paimpont (Brittany, France) and opens new interdisciplinary research opportunities.

BackgroundThe Biological Field Station of Paimpont (Station Biologique de Paimpont, SBP), owned by the University of Rennes and located in the Brocéliande Forest of Brittany (France), has been hosting student scientific research and field trips during the last 60 years. The study area of the SBP is a landscape mosaic of 17 ha composed of gorse moors, forests, prairies, ponds and creeks. Land use has evolved over time. Historical surveys by students and researchers focused on insects and birds. With this study, we aimed to increase the range of taxa observations, document changes in species composition and landscape and provide a basis for interdisciplinary research perspectives. We gathered historical data, implemented an all-taxon biodiversity inventory (ATBI) in different habitats of the SBP study area, measured abiotic factors in the air, water and soil and performed a photographical landscape observation during the BioBlitz held in July 2017.New informationDuring the 24 h BioBlitz, organised by the SBP and the EcoBio lab from the University of Rennes and the French National Center of Scientific Research (CNRS), different habitats were individually sampled. Seventy-seven experts, accompanied by 120 citizens and 12 young people participating in the European Volunteer Service, observed, identified and databased 660 species covering 5 kingdoms, 8 phyla, 21 classes, 90 orders and 247 families. In total, there were 1819 occurrences including records identified to higher taxon ranks, thereby adding one more kingdom and four more phyla. Historical data collection resulted in 1176 species and 4270 occurrences databased. We also recorded 13 climatic parameters, 10 soil parameters and 18 water parameters during the BioBlitz. Current habitats were mapped and socio-ecological landscape changes were assessed with a diachronic approach using 32 historical photographs and historical maps. The coupling of historical biodiversity data with new biotic and abiotic data and a photographic comparison of landscape changes allows an integrative understanding of how the SBP changed from agriculturally-used land to a managed natural area within the last 60 years. Hence, this BioBlitz represents an important holistic sampling of biodiversity for studies on trophic webs or on trophic interactions or on very diverse, but connected, habitats. The integration of social, biotic and abiotic data opens innovative research opportunities on the evolution of socio-ecosystems and landscapes.

Machine learning in energy economics and finance: A review

Machine learning (ML) is generating new opportunities for innovative research in energy economics and finance. We critically review the burgeoning literature dedicated to Energy Economics/Finance applications of ML. Our review identifies applications in areas such as predicting energy prices (e.g. crude oil, natural gas, and power), demand forecasting, risk management, trading strategies, data processing, and analyzing macro/energy trends. We critically review the content (methods and findings) of more than 130 articles published between 2005 and 2018. Our analysis suggests that Support Vector Machine (SVM), Artificial Neural Network (ANN), and Genetic Algorithms (GAs) are among the most popular techniques used in energy economics papers. We discuss the achievements and limitations of existing literature. The survey concludes by identifying current gaps and offering some suggestions for future research.

Long-Term Disaster Resilience: A Research Gap

Introduction:This research identified a gap in understanding the lived experience of long-term disaster resilience (LTDR). Increasing disasters could influence more people. Therefore, understanding LTDR becomes imperative. Little research documents men and women’s reflections following disasters. Current research highlights survivors’ mental health, particularly clinical diagnoses like PTSD. Research remains limited on the social impacts long after disasters.Aim:Research aimed to identify a gendered perspective of the lived experience about what contributes to LTDR three years after Ash Wednesday in 1983, the Victorian floods in 1993 and 2010-11, and the 2009 Black Saturday fires.Methods:A comprehensive, systematized search was conducted of peer-reviewed, grey, and secondary literature for a narrative review and thematic analysis.Results:106 references were identified. After removing duplicates and papers not fitting the inclusion criteria, two papers met the criteria. However, two borderline papers were included due to the closeness of the timeframe and brevity of research available.Discussion:Most research is related to the immediate aftermath or short-term resilience. Papers provided no specific attributes to enhance the lived experience of LTDR as it related to gender. However, factors that could enhance the lived experience of LTDR were drawn from six themes in sociological studies. Presumptive interpretations were made about what factors may provide insight into the social and contextual issues of LTDR. The literature dearth identified the need for long-term disaster resilience research. The most striking conclusion drawn from themes tells how people perceived the way a disaster and the ensuing period affected their personal relationships and circumstances. Overall, positive experiences strengthened their resilience while negative experiences hindered their resilience. While the review resulted in a disappointing outcome, the dearth of LTDR research lacked any reference to gender but confirmed research opportunities for innovative research that could influence policy and practice.

Machine Learning in Energy Economics and Finance: A Review

Machine Learning (ML) is generating new opportunities for innovative research in energy economics and finance. We critically review the burgeoning literature dedicated to Energy Economics/Finance applications of ML. Our review identifies applications in areas such as predicting energy prices (e.g. crude oil, natural gas, and power), demand forecasting, risk management, trading strategies, data processing, and analyzing macro/energy trends. We critically review the content (methods and findings) of more than 130 articles published between 2005 and 2018. Our analysis suggests that Support Vector Machine (SVM), Artificial Neural Network (ANN), and Genetic Algorithms (GAs) are among the most popular techniques used in energy economics papers. We discuss the achievements and limitations of existing literature. The survey concludes by identifying current gaps and offering some suggestions for future research.

Revisiting the etiological aspects of dissociative identity disorder: a biopsychosocial perspective.

Dissociative identity disorder (DID) is a chronic post-traumatic disorder where developmentally stressful events in childhood, including abuse, emotional neglect, disturbed attachment, and boundary violations are central and typical etiological factors. Familial, societal, and cultural factors may give rise to the trauma and/or they may influence the expression of DID. Memory and the construction of self-identity are cognitive processes that appear markedly and centrally disrupted in DID and are related to its etiology. Enduring decoupling of psychological modes may create separate senses of self, and metamemory processes may be involved in interidentity amnesia. Neurobiological differences have been demonstrated between dissociative identities within patients with DID and between patients with DID and controls. Given the current evidence, DID as a diagnostic entity cannot be explained as a phenomenon created by iatrogenic influences, suggestibility, malingering, or social role-taking. On the contrary, DID is an empirically robust chronic psychiatric disorder based on neurobiological, cognitive, and interpersonal non-integration as a response to unbearable stress. While current evidence is sufficient to firmly establish this etiological stance, given the wide opportunities for innovative research, the disorder is still understudied. Comparison of well-selected samples of DID patients with non-dissociative subjects who have other psychiatric disorders would further delineate the neurobiological and cognitive features of the disorder, whereas genetic research on DID would further illuminate the interaction of the individual with environmental stress. As such, DID may be seen as an exemplary disease model of the biopsychosocial paradigm in psychiatry.