Research Roadmap Research Articles

Harnessing immunity: Immunomodulatory therapies in COVID-19.

An overly exuberant immune response, characterized by a cytokine storm and uncontrolled inflammation, has been identified as a significant driver of severe coronavirus disease 2019 (COVID-19) cases. Consequently, deciphering the intricacies of immune dysregulation in COVID-19 is imperative to identify specific targets for intervention and modulation. With these delicate dynamics in mind, immunomodulatory therapies have emerged as a promising avenue for mitigating the challenges posed by COVID-19. Precision in manipulating immune pathways presents an opportunity to alter the host response, optimizing antiviral defenses while curbing deleterious inflammation. This review article comprehensively analyzes immunomodulatory interventions in managing COVID-19. We explore diverse approaches to mitigating the hyperactive immune response and its impact, from corticosteroids and non-steroidal drugs to targeted biologics, including anti-viral drugs, cytokine inhibitors, JAK inhibitors, convalescent plasma, monoclonal antibodies (mAbs) to severe acute respiratory syndrome coronavirus 2, cell-based therapies (i.e., CAR T, etc.). By summarizing the current evidence, we aim to provide a clear roadmap for clinicians and researchers navigating the complex landscape of immunomodulation in COVID-19 treatment.

Accuracy of Electronic Health Record Documentation of Parental Presence: A Data Validation and Quality Improvement Analysis.

Parental presence in the neonatal intensive care unit (NICU) is known to improve the health outcomes of an admitted infant. The use of the electronic health record (EHR) to analyze associations between parental presence and sociodemographic factors could provide important insights to families at greatest risk for limited presence during their infant's NICU stay, but there is little evidence about the accuracy of nonvital clinical measures such as parental presence in these datasets. A data validation study was conducted comparing the percentage agreement of an observational log of parental presence to the EHR documentation. Overall, high accuracy values were found when combining two methods of documentation. Additional stratification using a more specific measure, each chart's complete accuracy, instead of overall accuracy, revealed that night shift documentation was more accurate than day shift documentation (76.3% accurate during night shifts, 55.2% accurate during day shifts) and that flowsheet (FS) recordings were more accurate than the free-text plan of care (POC) notes (82.4% accurate for FS, 75.1% accurate for POC notes). This research provides a preliminary look at the accuracy of EHR documentation of nonclinical factors and can serve as a methodological roadmap for other researchers who intend to use EHR data.

Home country influence in cross-border mergers and acquisitions by emerging market firms: a systematic review

PurposeEmerging market firms (EMFs) are increasingly expanding their global presence through cross-border mergers and acquisitions (CBMAs). While such deals are distinct from those by advanced market firms, there is a need for a comprehensive understanding of how emerging home markets form this distinctiveness. This study aims to remedy this gap.Design/methodology/approachThe authors conduct a systematic literature review of 84 empirical papers. Through a broad search string and seven exclusion criteria, the authors carefully select studies on country-level home factors of CBMAs by EMFs.FindingsAfter summarizing paper volumes, journals and context factors of home/host countries and industries, the authors highlight different strands of institutional theory as the prevailing perspective and pre-M&A issues as the foremost theme. CBMAs by EMFs are influenced by distinct home-exclusive factors, and the mechanisms linking home-country factors to CBMAs by EMFs show significant inconsistencies across studies.Originality/valueThis review focuses on home country influence and thus goes beyond general characteristics of CBMAs by EMFs. The authors highlight more diverse types of home country factors and CBMA outcomes and, more importantly, take a closer look at involved mechanisms. Doing so, the authors identify gaps and disparities that have limited the understanding of home country influence in CBMAs by EMFs. To correct this, the authors offer a comprehensive roadmap for future research, contributing to EMF studies in particular and CBMA and international business research in general.

Multidisciplinary and multiscale nanoscience research roadmap based on large scientific facilities

Multidisciplinary and multiscale nanoscience research roadmap based on large scientific facilities

Advancements in Characterization Techniques for Microemulsions: From Molecular Insights to Macroscopic Phenomena.

Microemulsions are thermodynamically stable, optically isotropic, transparent, or semi-transparent mixed solutions composed of two immiscible solvents stabilized by amphiphilic solutes. This comprehensive review explores state-of-the-art techniques for characterizing microemulsions, which are versatile solutions essential across various industries, such as pharmaceuticals, food, and petroleum. This article delves into spectroscopic methods, nuclear magnetic resonance, small-angle scattering, dynamic light scattering, conductometry, zeta potential analysis, cryo-electron microscopy, refractive index measurement, and differential scanning calorimetry, examining each technique's strengths, limitations, and potential applications. Emphasizing the necessity of a multi-technique approach for a thorough understanding, it underscores the importance of integrating diverse analytical methods to unravel microemulsion structures from molecular to macroscopic scales. This synthesis provides a roadmap for researchers and practitioners, fostering advancements in microemulsion science and its wide-ranging industrial applications.

Navigating Energy and Financial Markets: A Review of Technical Analysis Used and Further Investigation from Various Perspectives

This review paper thoroughly examines the role of technical analysis in energy and financial markets with a primary focus on its application, effectiveness, and comparative analysis with fundamental analysis. The discussion encompasses fundamental principles, investment strategies, and emerging trends in technical analysis, underscoring their critical relevance for traders, investors, and analysts operating within these markets. Through the analysis of historical price data, technical analysis serves as a crucial tool for recognizing market trends, determining trade timing, and managing risk effectively. Given the complex nature of energy and financial markets, where many factors influence prices, the significance of technical analysis is particularly pronounced. This review aims to provide practical insights and serve as a roadmap for future research in the realm of technical analysis within energy and financial markets. This review contributes to the ongoing discourse and advancement of knowledge in this crucial field by synthesizing existing perspectives and proposing avenues for further exploration.

Effects of landscape changes on water quality: A global meta-analysis

Landscape changes resulting from anthropogenic activities and climate changes severely impact surface water quality. A global perspective on understanding their relationship is a prerequisite for pursuing equity in water security and sustainable development. A sequent meta-analysis synthesizing 625 regional studies from 63 countries worldwide was conducted to analyze the impacts on water quality from changing landscape compositions in the catchment and explore the moderating factors and temporal evolution. Results exhibit that total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) in water are mostly concerned and highly responsive to landscape changes. Expansion of urban lands fundamentally degraded worldwide water quality over the past 20 years, of which the arid areas tended to suffer more harsh deterioration. Increasing forest cover, particularly low-latitude forests, significantly decreased the risk of water pollution, especially biological and heavy metal contamination, suggesting the importance of forest restoration in global urbanization. The effect size of agricultural land changes on water quality was spatially scale-dependent, decreasing and then increasing with the buffer radius expanding. Wetland coverage positively correlated with organic matter in water typified by COD, and the correlation coefficient peaked in the boreal areas (r=0.82, p<0.01). Overall, the global impacts of landscape changes on water quality have been intensifying since the 1990s. Nevertheless, knowledge gaps still exist in developing areas, especially in Africa and South America, where the water quality is sensitive to landscape changes and is expected to experience dramatic shifts in foreseeable future development. Our study revealed the worldwide consistency and heterogeneity between regions, thus serving as a research roadmap to address the quality-induced global water scarcity under landscape changes and to direct the management of land and water.

A circular economy roadmap for solar photovoltaics

Solar photovoltaics (PV) and other clean energy technologies are increasingly being deployed as an environmentally responsible and economic approach to energy system decarbonization. The shift from fossil fuel-centric to material-centric equipment has also shifted the consideration and management of environmental and societal impacts across the technology life cycle. Societal desire to reduce raw material needs, mitigate supply chain social and governance challenges, and take advantage of the reuse and recycling capabilities of materials has enhanced interest in regenerative economic models, called circular economies (CEs). A recent critical review documented that solar PV is on a “path towards increased circularity,” but the need remains to expand activities beyond recycling to a broader set of environmental and policy activities to fully realize the benefits [1,2]. Thus, the goal of this research roadmap is to facilitate and accelerate the transition to a solar PV CE by 1) highlighting current opportunities for PV value chain stakeholders to adopt circular strategies and 2) assessing research and development (R&D) needs that can be addressed in the short term to advance a CE for the solar industry.

A Road Map for Population Health and Health Equity Research

This Viewpoint discusses the 4P’s Road Map for population health and health equity research.

Synergizing AI and Blockchain: Innovations in Decentralized Carbon Markets for Emission Reduction through Intelligent Carbon Credit Trading

This study aims to enhance the paradigm of decentralized carbon markets by proposing an innovative integration of artificial intelligence (AI) and blockchain technology for intelligent carbon credit trading with the goal of attaining sustainable emission reduction. Blockchain systems powered by artificial intelligence (AI) have the potential to boost the effectiveness of current systems and expedite the global implementation of emissions trading. Although still in its infancy, blockchain artificial intelligence (AI) presents a promising solution to some of the world's most pressing environmental issues. Environmental sustainability is greatly affected by artificial intelligence because of its decentralized computation architecture. The Artificial Intelligence and blockchain are outstanding direction for today’s environmental issues starting from carbon footprint emission to earth market unstable management, whereby the AI facilitates the best possible operational control of power systems and the blockchain offers decentralized trading platforms for the energy markets. The paper's theoretical framework, based on advanced mathematical models, serves as the foundation for this study, in which AI algorithms are methodically constructed to anticipate carbon emissions with unprecedented accuracy. Using sophisticated coding simulations and complicated mathematical formulas, the study boldly transitions into a realistic digital implementation that builds on this theoretical foundation. This complex experiment not only validates the theoretical ideas but also illustrates the complex relationship between blockchain and AI in the decentralized carbon market ecosystem. This experiment's mathematical basis is the creation of an integrated pricing model that seamlessly blends blockchain-based trading dynamics with AI-driven forecasts. The model incorporates a dynamic, self-adjusting system that responds to current market conditions, in addition to optimizing the pricing calculation of carbon credits. Complex market dynamics, player tactics, and the overall equilibrium of the carbon credit market are all modeled by mathematical simulations. The project goes deeper into building blockchain-based smart contracts, which enable safe and transparent transactions. The comprehensive mathematical results of the experiment shed light on the best way to price carbon credits while underscoring the disruptive potential of blockchain and artificial intelligence in terms of sustainable emission reduction strategies used in carbon markets. Major conclusions about the potential advantages of Blockchain AI for guaranteeing emissions reduction are drawn from the current study. Additionally, it presents a roadmap for future research in this area.

A Research Paper on Flexible Manufacturing System Simulator for Technical Institutes

Today’s scenario of manufacturing activities is toward automation. To provide practical exposure and better understand, there is need of automated machines in the technical institutes. Due to financial&amp; space constraints, most of the institutes is not having automated machines for the demonstration. This comprehensive review navigates the landscape of Flexible Manufacturing Systems (FMS), dissecting scheduling rules, design, and simulation methodologies, and the integration of advanced technologies. The motor control systems, microcontroller applications, and robotic process automation contribute to a holistic understanding of FMS evolution. This synthesis serves as a roadmap for researchers and practitioners, fostering adaptability and responsiveness in modern manufacturing. FMS is the highest automation in manufacturing industry and work on group technology concept. In this project, the authors are planning to design and develop FMS Simulator for technical institute. This helps students to understand group technology concept, automated vehicle system, automated material handling system, working of automated transfer line, industrial robot and so on. This proposed initiative aligns with the broader goals of academics – to produce graduates who are not only well- versed in theoretical concepts but also equipped with the practical skills demanded by industry The utilization of an FMS with educational purposes started in the academic year 2011–2012 and still remains active. Here, the most illustrative FPs are expounded, and successful academic outcomes are reported. In addition, a set of initial considerations based on the experience acquired by the FMS is provided.

Centering Digital Health Equity During Technology Innovation: Protocol for a Comprehensive Scoping Review of Evidence-Based Tools and Approaches.

In the rush to develop health technologies for the COVID-19 pandemic, the unintended consequence of digital health inequity or the inability of priority communities to access, use, and receive equal benefits from digital health technologies was not well examined. This scoping review will examine tools and approaches that can be used during digital technology innovation to improve equitable inclusion of priority communities in the development of digital health technologies. The results from this study will provide actionable insights for professionals in health care, health informatics, digital health, and technology development to proactively center equity during innovation. Based on the Arksey and O'Malley framework, this scoping review will consider priority communities' equitable involvement in digital technology innovation. Bibliographic databases in health, medicine, computing, and information sciences will be searched. Retrieved citations will be double screened against the inclusion and exclusion criteria using Covidence (Veritas Health Innovation). Data will be charted using a tailored extraction tool and mapped to a digital health innovation pathway defined by the Centre for eHealth Research roadmap for eHealth technologies. An accompanying narrative synthesis will describe the outcomes in relation to the review's objectives. This scoping review is currently in progress. The search of databases and other sources returned a total of 4868 records. After the initial screening of titles and abstracts, 426 studies are undergoing dual full-text review. We are aiming to complete the full-text review stage by May 30, 2024, data extraction in October 2024, and subsequent synthesis in December 2024. Funding was received on October 1, 2023, from the Centre for Health Equity Incubator Grant Scheme, University of Melbourne, Australia. This paper will identify and recommend a series of validated tools and approaches that can be used by health care stakeholders and IT developers to produce equitable digital health technology across the Centre for eHealth Research roadmap. Identified evidence gaps, possible implications, and further research will be discussed. DERR1-10.2196/53855.

Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues

Since its introduction in November 2022, ChatGPT has rapidly gained popularity due to its remarkable ability in language understanding and human-like responses. ChatGPT, based on GPT-3.5 architecture, has shown great promise for revolutionizing various research fields, including code generation. However, the reliability and quality of code generated by ChatGPT remain unexplored, raising concerns about potential risks associated with the widespread use of ChatGPT-driven code generation. In this article, we systematically study the quality of 4,066 ChatGPT-generated programs of code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal of this work is threefold. First, we analyze the correctness of ChatGPT on code generation tasks and uncover the factors that influence its effectiveness, including task difficulty, programming language, time that tasks are introduced, and program size. Second, we identify and characterize potential issues with the quality of ChatGPT-generated code. Last, we provide insights into how these issues can be mitigated. Experiments highlight that out of 4,066 programs generated by ChatGPT, 2,756 programs are deemed correct, 1,082 programs provide wrong outputs, and 177 programs contain compilation or runtime errors. Additionally, we further analyze other characteristics of the generated code through static analysis tools, such as code style and maintainability, and find that 1,930 ChatGPT-generated code snippets suffer from maintainability issues. Subsequently, we investigate ChatGPT’s self-repairing ability and its interaction with static analysis tools to fix the errors uncovered in the previous step. Experiments suggest that ChatGPT can partially address these challenges, improving code quality by more than 20%, but there are still limitations and opportunities for improvement. Overall, our study provides valuable insights into the current limitations of ChatGPT and offers a roadmap for future research and development efforts to enhance the code generation capabilities of artificial intelligence models such as ChatGPT.

Institutional implications for science and industrial capacity: policy lessons from the UK’s pandemic response

Abstract Global shortages of critical equipment and supplies induced by COVID-19 forced countries to rapidly build and ramp up their indigenous testing and production capacities. However, the many ways in which institutional and organizational change occurred has not been sufficiently captured. Building domestic capacity requires the leveraging and repurposing of existing domestic scientific and technological capabilities, coupled with intensified global outreach to new and existing partners and suppliers. Using the framework of institutional variety, this paper looks at two facets of the UK’s COVID emergency industrial response: (1) building its laboratory testing capabilities and (2) for increasing production of personal protective equipment; assessing the institutional capacities and relations that were leveraged in this regard. It uses these findings together with observations of ‘innovation processes under emergency conditions’ and the potential uses of a ‘critical equipment policy’ to sharpen some of the recommendations made in the UK’s post-COVID Research and Development Roadmap.

Relationship between deposition techniques and nanoparticle dispersions for flexible and printed electronics

Reverse micelles composed of polystyrene-b-poly(2-vinylpyiridine) have been used to synthesize nanoparticles composed of a wide range of materials, including metals, metal oxides, dielectrics, semiconductors perovskites, and core–shell nanoparticles. In this contribution, we examine the effect of deposition parameters on two-dimensional nanoparticle arrangements from colloidal solutions created using spin coating, dip coating, slot-die coating, and electrospray deposition. Despite the importance of achieving uniform coatings of ordered arrays of colloidal particles, previous studies have not thoroughly addressed this challenge. We show that the adjustability of interparticle distance depends on the deposition technique used and only occurs within the stable defect-free operating window of the deposition parameters. Establishing the specific operating window for each technique for a model system, we propose general guidelines that can be used for ensuring uniform coatings regardless of precursor loading and provide a guide for adjusting the deposition conditions when coating defects occur. We introduces a novel application of ellipsometry to evaluate interparticle spacing in nanoparticle arrays, enhancing our ability to assess film uniformity, allowing for quick and easy tuning of nanoparticle dispersion. Comparisons between spin, dip, and slot-die coating techniques reveal insights into the correlation between interparticle spacing and ordering, highlighting the importance of fitting relationships for various coating samples. This comprehensive comparison and discussion provide a roadmap for future research, outlining current challenges and trends and offering insights into achievable spacings and ordering in coating processes. This allows the classification of various deposition techniques with respect to their suitability for tailored applications.

Diffractive deep neural networks: Theories, optimization, and applications

Optical neural networks (ONN) are experiencing a renaissance, driven by the transformative impact of artificial intelligence, as arithmetic pressures are progressively increasing the demand for optical computation. Diffractive deep neural networks (D2NN) are the important subclass of ONN, providing a novel architecture for computation with trained diffractive layers. Given that D2NN directly process light waves, they inherently parallelize multiple tasks and reduce data processing latency, positioning them as a promising technology for future optical computing applications. This paper begins with a brief review of the evolution of ONN and a concept of D2NN, followed by a detailed discussion of the theoretical foundations, model optimizations, and application scenarios of D2NN. Furthermore, by analyzing current application scenarios and technical limitations, this paper provides an evidence-based prediction of the future trajectory of D2NN and outlines a roadmap of research and development efforts to unlock its full potential.

Bibliometric Analysis of Trends and Patterns in Sustainable and Responsible Investments (SRI) Research from 2000-2024

This study provides a comprehensive bibliometric analysis of Sustainable and Responsible Investments (SRI) research from 2000 to 2024, utilizing data visualization tools to map the evolution and current state of the field. Through thematic clusterization, temporal trends, author collaboration networks, and potential research topics, the research offers insights into the dynamic nature of SRI. Initial findings show a progression from foundational concepts to more nuanced discussions around impact investing and sustainability indices. The analysis of author collaboration networks highlights key influencers and suggests potential areas for future collaboration. Additionally, the identification of underexplored areas such as ethical investment frameworks and the impact of social investments indicates new directions for research. This paper underscores the importance of SRI in aligning financial investments with global sustainability goals, providing a roadmap for future research and practice in the field.

Watershed Resilience: Quantification Methods and Future Perspectives

In recent years, watershed resilience has garnered a substantial interest driven by the need to sustainably manage vital ecosystems in the face of increasing pressures such as climate change and land-use alterations, leading to assortment of definitions and assessment approaches. This overabundance has occasionally manifested in ambiguity and, at times, contributed improper implementations. This review evaluates the capacities, and frameworks employed in quantifying watershed resilience across various geographical contexts. It synthesizes the current state of knowledge to identify trends, limitations, and areas requiring further investigation. Due to the limited number of prior researches synthesizing watershed resilience quantification methods, the primary contribution of this study lies in its consolidation and synthesis of diverse research efforts, shedding light on the evolving landscape of watershed resilience quantification. By critically examining the strengths and weaknesses of existing definitions and adopted frameworks, we aim to provide a roadmap for future research in this field. Additionally, this review emphasizes the importance of developing standardized indicators and frameworks to facilitate more robust and comparative assessments of watershed resilience. A critical research gap is the lack of a universally accepted assessment framework for watershed resilience, hindering comparability and decisionmaking. We advocate for interdisciplinary collaboration to establish a common framework integrating ecological, hydrological, and social aspects of resilience. In conclusion, this review underscores the urgent need to advance watershed resilience quantification and offers a clear research agenda. Addressing research gaps and fostering interdisciplinary collaboration can significantly contribute to the evolving field of watershed resilience assessment and management.

Voluntary Disclosures and their Drivers: A Study of MDA Reports in India

The aim of this study is to examine the impact of corporate characteristics on voluntary disclosures of management discussion and analysis (MD&amp;A) reports in India. Using a formal tone, the data was extracted from the annual reports of the top 100 listed firms available on the CMIE Prowess database for seven years (2016–2022). After excluding 23 companies from the financial and insurance sector, a panel regression method with the assistance of Gretl software was employed to investigate the relationship between the Management Discussion and Analysis Disclosure Index (MDADI) for voluntary aspects and various corporate attributes, with a total of 490 firm years of balanced observations. In India, firms follow the mandatory compliance of the MD&amp;A reports, but voluntary disclosures are somehow those which are not much emphasized but are a good indication of firm performance and their accountability towards their stakeholders (Mayew et al., 2015). Our empirical findings reveal that profitability as a proxy to firm performance has a significant positive relationship with MD&amp;A voluntary disclosures. Further, an insignificant association between VDS (Voluntary Disclosure Score) and the board size, presence of independent directors and firm size was found. This indicates that firm performance plays a significant role in adding more voluntary disclosures in MD&amp;A reports. The possible reason for this could be the use of “Management Impression Strategy” in the MD&amp;A reports, which means managers disclose more only when the firm has earned more and use impressive language to attract stakeholders. The outcomes of this research offer valuable insights for regulators, policymakers, and listed companies in India, aiding in the enhancement of MD&amp;A reporting quality. Additionally, this study provides a roadmap for future research on MD&amp;A reporting quality and corporate attributes in other emerging countries that have similar regulatory frameworks. This paper makes a timely and pertinent contribution to the scholarly discourse by shedding light on the relationship between MD&amp;A disclosures and firm attributes. Its findings provide valuable insights for both academia and industry.

Ethical implementation of artificial intelligence in the service industries

ABSTRACT This study employs a systematic literature review (SLR) combined with bibliometric analysis to investigate the ethical implementation of Artificial Intelligence (AI) in the service industries. This research uncovers key challenges such as privacy, bias, transparency, and accountability, emphasizing the critical need for ethical AI practices in service sectors handling sensitive customer data. Findings reveal that AI's ethical implementation is crucial in areas like decision support, customer engagement, automation, and new service development. The analysis provides actionable insights into enablers, including ethical guidelines, human oversight, comprehensive training, and adaptive organizational culture, which are essential for unlocking AI's potential and mitigating risks. The study offers a roadmap for future research, advocating interdisciplinary collaboration, customer co-creation in ethical frameworks, and sector-specific policy adaptation, ultimately aiming to build responsible and trustworthy AI in the service industries.