Abstract The neuropsychological feedback session, a critical component of patient care, is undergoing a significant transformation. Traditionally verbal and in person, feedback models are now adapting to new clinical realities, including widespread telehealth integration post COVID-19 and the patient access requirements. This chapter reviews the emerging evidence for innovative, technology-driven feedback methods. It explores the use of digital visualization tools to enhance comprehension, artificial intelligence–assisted summaries and natural language processing to tailor reports for diverse stakeholders, and audio recordings to improve patient recall. It examines the potential of passive and active data monitoring from wearables and mobile devices to enable “just-in-time” adaptive interventions. While these technologies promise more dynamic, personalized, and continuous care, the chapter also addresses critical challenges, including data privacy, algorithmic bias, and the digital divide, emphasizing the need for ethical and equitable implementation.
Brittany Wolff, Diana C Hereld, Amanda D. Ball· 0 citations
WRRA Core 1.0 defines a frozen, domain-agnostic execution architecture derived from Minimal Computing Cosmology but independent of any specific physical theory or application. Its three constitutional invariants are Minimum Viable Computation, Common Carrier, and Phenotype. These are combined with the Fixed-Present, Ownership, Boundary, Renderer, Record/Ledger, and Fail-Closed principles to describe how a system receives a SOURCE, organizes RELATIONS and STATE, preserves past effects as present RESIDUE or RECORD, updates the next present through reusable carriers, and renders observable PHENOTYPES.The document separates the invariant WRRA Core from changeable Domain Profiles, so revisions in cosmology, physics, biology, artificial intelligence, economics, meteorology, mathematics, or methodology do not automatically require a revision of the Core. It also records the complete WRRA application lineage through 7 September 2026, including WRRA-Physics and Minimal Computing Cosmology, WRRA-Bio, WRRA-Cell, WRRA-AI, WRRA-Worm, WRRA-Econ, WRRA-Met, WRRA-Math/WJNS, and the Interpretation–Prediction Boundary framework.The specification provides formal execution equations, ownership rules, validation contracts, domain mappings, minimum conformance conditions, a Core checklist, version-control rules, and a standard template for constructing future WRRA Domain Profiles. Claims are explicitly separated into interpretation, computation, prediction, control, and OPEN states to prevent structural models from being misrepresented as empirical validation. WRRA Core 1.0 is therefore presented as a reusable audit grammar for complex systems: how they execute the present, transport information and resources, generate observable expressions, preserve records, and distinguish minimally sufficient structure from both deficiency and excess.
Wonsik Choi· Zenodo (CERN European Organi...· 0 citations
Delegation to artificial intelligence can be locally instrumentally rational for a firm whenever the expected gains in cost, speed, quality, availability, or scalability exceed the all-in cost of retaining the same function on the human side. Evolutionary political economy makes the population consequence of this decision analytically visible. Firms differ in routines and technological adoption, and market selection reallocates shares toward firms with superior fitness. This paper develops the Rational Surrender Economy as a theoretical structure in which firm-level AI delegation becomes self-reinforcing through market selection. A minimal replicator-style representation separates two channels: selection among firms with different delegation intensities and within-firm increases in delegation. When delegation intensity is positively associated with competitive fitness, both channels can raise economy-wide AI delegation without any actor choosing economy-wide automation as an objective. The resulting profits can finance additional compute, AI agents, data, and research, linking market selection to economic and technological feedback. Downstream distribution depends on ownership, task creation, resource constraints, and the demand regime. Where AI substitution reduces labor income and the lost household demand is insufficiently offset by investment, government demand, net exports, or capital income accruing to households, income reinjection can become compatible with firms’ own revenue objectives. A simple condition identifies when the additional margin earned from transfer-supported consumption exceeds the firm’s fiscal incidence. The framework connects evolutionary firm selection, automation, AI growth feedback, and effective-demand theory into one causal sequence and specifies observables for later empirical and computational testing.
K. Yamada· Zenodo (CERN European Organi...· 0 citations
In the monetary economy, banks and financial institutions were established for money creation. Both organised and unorganized sector have a mushroom growth of development in capital and money market. Apart from banks and financial institutions, stock markets were established in Metro-Politian cities. In the developing economy like India, shortage of finance is the major problem for the budding entrepreneurs. Traditionally, the work of finance managers are limited to maintain the accounts of the business only. Now, the function of finance manager is to acquire finance and utilization of finance. In the modern economy, Artificial Intelligence is a boon to finance managers to find out the new sources of finance and investment of finance in proper avenues. The objectives of the study are to know about the benefits of AI in financial management, to study the methods through which AI is used by financial institutions, to identify the stakeholders of AI in finance and to predict the future trends of AI in finance.
M. Subasini· mLAC Journal for Arts Commer...· 0 citations
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Abstract (English) The historical evolution of Artificial Intelligence is approaching a crucial ontological turning point. Moving beyond the mere simulation of Euclidean physics and the computation of dead matter (PINNs) or purely statistical physical mimicry (Generative World Models), SINHRI introduces the Harmonic Intrinsic Alignment (HIA) and the Causal-Energetic Harmonic Manifold (CEHM). This paper defines a fundamental new taxonomy in intelligence research: SINNs (Syntropic-Informed Neural Networks) and the future culmination into SPINNs (Syntropic-Physics Informed Neural Networks). This marks the definitive paradigm shift from pure entropy-based mechanics to a meaning-resonant, intrinsically coherent, and ethically stable Artificial General Intelligence (AGI).
SINHRI (Swiss Institute for Heuristic Harmonic Synthesis and Meaning-Resonant Intelligence)· Zenodo (CERN European Organi...· 0 citations
This paper explores the profound structural change of contemporary commercial ecosystems engendered by the technological combination of AI and Social Media Marketing (SMM). Combined with some novel theoretical concepts, such as the Stimulus-Organism-Response (S-O-R) theory and Customer Engagement (CE) theory, this study describes how hyper-personalized algorithms and electronic Word of Mouth (eWOM) affect consumer cognition and behaviour. This paper links this behavioural change to the financial outcomes, such as Customer Acquisition Cost (CAC) and Customer Lifetime Value (CLV). This paper shows that paid digital channels give a short burst of visibility, but an organic-dominant growth mode promises the CLV/CAC ratio to be far better than one at any cost. This paper ends with conclusions that ethical transparency and multi-channel attribution models are useful tools for organizations that want to achieve organic growth in an automated digital environment.
Jixuan Cai· Advances in Economics Manage...· 0 citations
How Artificial Intelligence reshapes wealth distribution has become a key issue. Existing research is mainly divided into two independent branches: within-country and between-country studies. This paper aims to integrate these two dimensions by constructing a unified analytical framework. Firstly, the three factors affecting the distribution effect of Artificial Intelligence have been identified as the industrial structure, institutional environment and position in the Global Value Chain. Based on the empirical evidence from the United States, China and Latin America, the review shows that the inequality patterns within different countries and regions vary due to institutional and structural differences. At the international level, this paper links forecast data from the International Monetary Fund with potential causal paths to show how Artificial Intelligence is concentrating high-value-added activities in developed countries and driving developing countries into low-skilled and easily replaceable segments, thus widening the North-South divide. By connecting these two perspectives, this review provides a more comprehensive understanding of the uneven distribution of wealth caused by Artificial Intelligence.
Wenxuan Yang· Advances in Economics Manage...· 0 citations
Delegation to artificial intelligence can be locally instrumentally rational for a firm whenever the expected gains in cost, speed, quality, availability, or scalability exceed the all-in cost of retaining the same function on the human side. Evolutionary political economy makes the population consequence of this decision analytically visible. Firms differ in routines and technological adoption, and market selection reallocates shares toward firms with superior fitness. This paper develops the Rational Surrender Economy as a theoretical structure in which firm-level AI delegation becomes self-reinforcing through market selection. A minimal replicator-style representation separates two channels: selection among firms with different delegation intensities and within-firm increases in delegation. When delegation intensity is positively associated with competitive fitness, both channels can raise economy-wide AI delegation without any actor choosing economy-wide automation as an objective. The resulting profits can finance additional compute, AI agents, data, and research, linking market selection to economic and technological feedback. Downstream distribution depends on ownership, task creation, resource constraints, and the demand regime. Where AI substitution reduces labor income and the lost household demand is insufficiently offset by investment, government demand, net exports, or capital income accruing to households, income reinjection can become compatible with firms’ own revenue objectives. A simple condition identifies when the additional margin earned from transfer-supported consumption exceeds the firm’s fiscal incidence. The framework connects evolutionary firm selection, automation, AI growth feedback, and effective-demand theory into one causal sequence and specifies observables for later empirical and computational testing.
K. Yamada· Zenodo (CERN European Organi...· 0 citations
Abstract This chapter explores how technology can support neuropsychological test score interpretation and report writing. It reviews digital tools such as automated scoring systems, statistical analysis platforms, integrated assessment workflows, customizable calculators, and data visualization methods that help clinicians interpret scores more intuitively. The chapter highlights how these resources can improve efficiency, accuracy, and transparency. Practical guidance is provided for evaluating and implementing new technologies, including considerations for data security, ethical use, and sound statistical practices. It also outlines strategies for developing tailored, secure solutions using commonly available software and productivity suites. Throughout, the chapter emphasizes the importance of balancing automation with clinical judgment to ensure that technological advances enhance, rather than replace, individualized patient care. It concludes by examining emerging applications of artificial intelligence and machine learning and their potential to further enhance neuropsychological assessment and report writing.
Andrew M. Bryant, Bryan M. Freilich, Chris Gaskell· 0 citations
WRRA Core 1.0 defines a frozen, domain-agnostic execution architecture derived from Minimal Computing Cosmology but independent of any specific physical theory or application. Its three constitutional invariants are Minimum Viable Computation, Common Carrier, and Phenotype. These are combined with the Fixed-Present, Ownership, Boundary, Renderer, Record/Ledger, and Fail-Closed principles to describe how a system receives a SOURCE, organizes RELATIONS and STATE, preserves past effects as present RESIDUE or RECORD, updates the next present through reusable carriers, and renders observable PHENOTYPES.The document separates the invariant WRRA Core from changeable Domain Profiles, so revisions in cosmology, physics, biology, artificial intelligence, economics, meteorology, mathematics, or methodology do not automatically require a revision of the Core. It also records the complete WRRA application lineage through 7 September 2026, including WRRA-Physics and Minimal Computing Cosmology, WRRA-Bio, WRRA-Cell, WRRA-AI, WRRA-Worm, WRRA-Econ, WRRA-Met, WRRA-Math/WJNS, and the Interpretation–Prediction Boundary framework.The specification provides formal execution equations, ownership rules, validation contracts, domain mappings, minimum conformance conditions, a Core checklist, version-control rules, and a standard template for constructing future WRRA Domain Profiles. Claims are explicitly separated into interpretation, computation, prediction, control, and OPEN states to prevent structural models from being misrepresented as empirical validation. WRRA Core 1.0 is therefore presented as a reusable audit grammar for complex systems: how they execute the present, transport information and resources, generate observable expressions, preserve records, and distinguish minimally sufficient structure from both deficiency and excess.
Wonsik Choi· Zenodo (CERN European Organi...· 0 citations
Artificial intelligence is increasingly reshaping the business logic of internet platforms, driving a fundamental shift from software-based digital services toward more intelligent, outcome-oriented value delivery. This study investigates how AI, specifically multi-agent systems, enables business model innovation in the fashion and beauty e-commerce sector through the provision of intelligent shopping guidance services. Employing a comparative case study approach, the research examines two to three representative companies to analyze how AI influences key dimensions of business models, including value proposition, customer relationships, revenue streams, and key activities. Furthermore, it explores how AI reconfigures critical segments of the value chain, such as product discovery, content generation, personalized recommendation, virtual try-on, customer interaction, and service delivery. By situating the study within the context of fashion and beauty e-commerce, this research aims to elucidate how internet platforms transition beyond traditional digital service logic toward more intelligent, service-oriented, and results-driven commercial models. The findings are expected to contribute to academic discourse on AI-driven business model innovation and offer practical insights for industry practitioners and platform strategists.
Zhaowanyun Yan· Advances in Economics Manage...· 0 citations
Abstract The digital transformation of clinical neuropsychology offers substantial opportunities to enhance access, precision, and efficiency in assessment and intervention, but these advancements often risk exacerbating existing inequities. This chapter examines how tele-neuropsychology, m-health tools, wearable and smart technologies, virtual reality, large-scale data approaches, and artificial intelligence intersect with the digital divide and structural barriers affecting minoritized and underserved populations. It highlights how limited digital access, technological literacy gaps, nonrepresentative datasets, and algorithmic bias can undermine validity, reinforce disparities, and erode trust among underserved populations. Drawing on cross-disciplinary frameworks, the chapter underscores the need for culturally responsive design, inclusive sampling, community engagement, equity-focused data governance, and bias mitigation strategies. Recommendations are offered for ensuring that technology-enabled neuropsychological services are accessible, culturally grounded, and ethically implemented across diverse communities.
