Integrated Research Field Research Articles

Law and Macro Finance

Abstract This article makes the case for developing law and macro-finance as an integrated research field that explores the role of credit and its regulation within the economic cycle. It highlights the importance of macro-financial conditions, such as leverage and liquidity, over traditional concerns like agency costs and efficiency when assessing the economic impact of the law. The article argues that the future of law and macro-finance research should focus on demonstrating (1) how the law operates under different macro-financial conditions, (2) how the law can shape these conditions, and (3) how these conditions influence legal frameworks. This research is partly inspired by, and could also be valuable for, organizations like the World Bank and IMF, which increasingly acknowledge the role of debt in financial crises and are beginning to promote policies that help countries better align the size of their credit markets with their economic needs.

Drug–Target Interaction Deep Learning-Based Model Identifies the Flavonoid Troxerutin as a Candidate TRPV1 Antagonist

Based on the advances made by artificial intelligence (AI) technologies in drug discovery, including target identification, hit molecule identification, and lead optimization, this study investigated natural compounds that could act as transient receptor potential vanilloid 1 (TRPV1) channel protein antagonists. Using a molecular transformer drug–target interaction (MT-DTI) model, troxerutin was predicted to be a TRPV1 antagonist at IC50 582.73 nM. In a TRPV1-overexpressing HEK293T cell line, we found that troxerutin antagonized the calcium influx induced by the TRPV1 agonist capsaicin in vitro. A structural modeling and docking experiment of troxerutin and human TRPV1 confirmed that troxerutin could be a TRPV1 antagonist. A small-scale clinical trial consisting of 29 participants was performed to examine the efficacy of troxerutin in humans. Compared to a vehicle lotion, both 1% and 10% w/v troxerutin lotions reduced skin irritation, as measured by skin redness induced by capsaicin, suggesting that troxerutin could ameliorate skin sensitivity in clinical practice. We concluded that troxerutin is a potential TRPV1 antagonist based on the deep learning MT-DTI model prediction. The present study provides a useful reference for target-based drug discovery using AI technology and may provide useful information for the integrated research field of AI technology and biology.

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.

The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.

Chemical Biology: Powerful Synergy Between Two Cultures

Chemical Biology: Powerful Synergy Between Two Cultures

Facilitating organisational decision making: a change risk assessment model case study

Purpose This paper aims to take the challenge to propose a novel modelling approach named Change Risk Assessment Model (CRAM), which will contribute significantly to the missing formality of business models especially in the change risk assessment area and decision-making. Organisational change risks are assessed with the aid of analytic hierarchy process (AHP) in an attempt to define the internal dynamics of organisational change management within project management eliciting also risk cause-and-effect relationships. Design/methodology/approach The study discusses interviews/survey/AHP. Findings The study presents the following findings. Change risk factors assessment (identification and prioritisation) recommendations (see Case Study) integration of change management; project management; risk management top four risk factors, namely, leadership, communication, project management team and culture. Research limitations/implications As projects can be different in a variety of factors (quality, scope), an exhaustive list of risk factors cannot be identified. There is a continuous risk identification process throughout the projects’ life cycle. For example, many risks can be classified initially as unknown and can be refined after the initiation phase of the project. AHP factors limitation (eight per level) possible bias (survey analysis). Practical implications With the aid of modelling and especially CRAM, business change risks can be assessed numerically and prioritised. Several risk factors and related attributes were identified and categorised. This empowers project managers or other stakeholders to make proper decisions about whether to take on or abandon respective organisational or project changes. Social implications One of the values of CRAM is that it can be regarded as a global change risk assessment method that can be applied regardless of project type, size or organisation. Moreover, it has the advantage that it can be used by any kind of project, as the method is designed to be tailored to specific needs, taking significant environmental change risk factors into account. AHP has numerous uses in operational research, in project management and in general in areas where decisions (evaluation and selection) have to be made. The analysis of the case study presented, indicated that it is vital to assess the degree (impact) that each risk attribute poses to address complex organisational decisions. Originality/value CRAM aims to bridge the gap between theoretical and applied work in the integrated research field of change management, project management and risk management. Furthermore, the approach attempts to develop a novel systematic methodology (model) for assigning probabilities in attributes (criteria) pair-wise comparison and more specifically, modelling and assessing change management risks, adding a different perspective and technique to the research area.

Astronomy and Anthropology : Toward an Integrated Research Field

Astronomy and Anthropology : Toward an Integrated Research Field

Between new genetic discoveries and large randomized trials--neurological research in the era of systems medicine.

The 2012 Eibsee meeting on 'Cellular Mechanisms of Neurodegeneration' addressed the need to integrate research on classical neurodegenerative mechanisms with investigations that relate to the immunological, glial and vascular sequels that accompany and often propagate neuronal injury. We report on the central topics that were addressed and discuss future directions towards establishing 'systems neurology' as a new integrated research field.

Accomplishments and Compromises in Prediction Research for World Records and Best Performances in Track and Field and Swimming

The conductors of this study reviewed prediction research and studied the accomplishments and compromises in predicting world records and best performances in track and field and swimming. The results of the study showed that prediction research only promises to describe the historical trends in track and field and swimming performances, to study the limits of human body based on current data, to examine factors that affect human's running, jumping, throwing, and swimming, and to understand the characteristics of human beings. Prediction research cannot accurately predict new world records and future best performances. In the future, prediction research should become an integrated research field consisting of different specialty areas. Researchers need to develop a better model in which random variables could be separated out as independent variables in order to reflect the complex interaction effects and to understand the nature, characteristics, and limitations of humans using world record/best performance data.

The public administration turn in integration research

ABSTRACT This article highlights how the study of public administration is brought back into the study of European integration and European Union (EU) governance. The public administration turn in integration research has brought generic insights into the broader field of public administration but has also brought theories, concepts and hypotheses from public administration into the field of integration research. The purpose of this overview is to reveal the variegated and rich research agendas in public administration research and to stimulate further research. This public administration turn highlights (i) the impact of the formal organization of core executive institutions such as the European Commission; (ii) the conditional autonomy of subordinate administrative units such as EU-level agencies; (iii) the integration of multi-level administrative systems through collegial structures such as EU-level committees; and (iv) the external penetration and differentiated impact of EU-level institutions on domestic public administration. The lack of systematic knowledge about the impact of administrative structures within EU-level and domestic public administration is thus steadily reduced.