In spite of substantial R&D expenditures, extended development timelines, and high uncertainty, unprofitable technology companies in biotechnology, semiconductors, and artificial intelligence (AI) have become important drivers of global innovation. As such, this paper explores these three industries and examines the adaptability and optimization paths of four mainstream valuation approaches, including discounted cash flow (DCF) valuation, real options valuation (ROV), relative valuation, and venture capital (VC) valuation. Using a core-and-supplementary structure, with biotechnology as the core focus and the other two industries as supporting cases, this study explores targeted improvements for each valuation method and verifies their applicability through theoretical analysis and empirical evidence. The results show that the four methods have significant differences in industry adaptability, thereby leading to the proposal of a stage-differentiated valuation framework. In particular, early-stage enterprises should prioritize the improved VC and ROV methods, mid-stage enterprises should focus on optimized real options valuation and risk-adjusted discounted cash flow valuation, while late-stage pre-IPO enterprises should adopt improved discounted cash flow valuation as the primary approach supplemented by improved relative valuation. This study enriches the valuation theory for unprofitable technology firms and provides practical insights for investors and enterprises.
Tongyu Wang· Advances in Economics Manage...· 0 citations
This study examines the application of innovative auspicious pattern design under a digital technology integration perspective. Moreover, the significant findings show that cultural symbol immersive experience could indicate a core independent variable shaping consumer behavior. Furthermore, the study demonstrates that consumer purchase decision-making and brand attachment appear to function as key dependent variables in this analytical framework. Given that the evidence demonstrates a chain structure, the results show that a four-stage mediation model could establish the pathway: digital immersive experience → cultural identity activation → product value perception → behavioral decision-making. Study shows three methods combined: AR (Augmented Reality) experiment, physiological measurement, questionnaire survey. However, the significant methodological findings show that data could indicate important patterns across 500 Chinese consumers examined in this study. Additionally, the results demonstrate that the study systematically explores how the immersive experience quality of AIGC (Artificial Intelligence Generated Content)-generated auspicious patterns might indicate differential activation of cultural identity. In light of these significant results, the findings show that this activation subsequently shows that shape consumer behavior in relevant ways. Evidence shows AR immersion links cultural identity activation. Thus, the key results show that experiential value has risen to become the primary dimension driving value perception in digital immersive contexts (β = 0.68). Notwithstanding this important result, the evidence shows that willingness to pay a price premium could demonstrate stronger prediction of brand commitment than purchase intention (β = 0.52). Data shows pathway distinct, fosters long-term brand attachment.
Shanpeng Xiao, Sharih Ahmad Mohamad, Nur Haizal Mat Yaakob· Digital Technologies Researc...· 0 citations
Abstract Artificial intelligence (AI) is increasingly influencing the practice of clinical neuropsychology, offering new tools to enhance efficiency, organization, and precision across every stage of the neuropsychological workflow. This chapter explores how AI can be applied responsibly from pre-evaluation preparation through data management, interpretation, and report writing. Examples illustrate how, under the clinician’s supervision, AI supports the synthesis of extensive records, the structuring of interviews, the organization of test data, and the refinement of report language. Throughout, attention is given to ethical and professional safeguards, including confidentiality, transparency, bias mitigation, informed consent, and documentation of AI-assisted contributions. The chapter emphasizes that AI must remain an augmentative instrument rather than an interpretive authority, with human expertise guiding all analytic and clinical decisions. Collectively, these considerations demonstrate how AI can ethically extend, rather than erode, the neuropsychologist’s role. Effective use of AI can enhance clarity, efficiency, and scientific rigor while preserving the discipline’s defining commitment to human judgment and individualized care.
Abstract This chapter examines the development and evolution of telerehabilitation efforts to address the common behavioral comorbidities of neurological conditions affecting children. It considers interventions across the developmental span of early childhood through adolescence, contrasting the evidence and utility of decontextualized telerehabilitation approaches such as drill-based cognitive training versus family-centered treatments that emphasize environmental supports and effective communication. Particular emphasis is given to online family problem-solving/Teen Online Problem-Solving (TOPS) for adolescents and I-InTERACT/InTERACT-North for younger children with emotional dysregulation and behavioral challenges associated with a range of neurological conditions. This chapter reviews intervention acceptability from the perspective of patients, families and clinicians, accessibility and efficacy, and transdiagnostic applications. It also highlights the value of integrating online programs into neuropsychology training programs from the perspectives of multiple stakeholders and explores multinational trials and implementation. Finally, it explores future directions, including the use of artificial intelligence and online, pediatric neurorehabilitation.
Shari L. Wade, Anna Adlam, Nicole Viola et al.· 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
Cardiovascular diseases (CVDs) remain the leading cause of death globally, driven by misinformation, low health literacy, and fragmented patient engagement. Existing digital health solutions often lack personalized, bilingual responsive education and proactive risk assessment, especially for vulnerable populations. To address these gaps, this study introduces a newly developed, fully integrated cardiovascular care system that combines a Digital Human Agent (DHA), Artificial Intelligence (AI)-based cardiovascular risk assessment (the focus of this study is on Atherosclerotic Cardiovascular Disease (ASCVD)), bilingual voice interaction via Natural Language Processing (NLP), and a real-time cardiologist dashboard; all within a single system. This is a comprehensive system designed, accessible via mobile and web, to deliver personalized, bilingual cardiovascular educational content and management. It employs the validated ASCVD model aligned with American Heart Association (AHA)/American College of Cardiology (ACC) guidelines and features an adaptive AI assistant that simplifies medical terms and offers risk-based recommendations. The system ensures Health Insurance Portability and Accountability Act (HIPAA)-compliant data security. Initial system evaluation and expert feedback reflect that the proposed system has the potential to enhance user engagement, support comprehension of cardiovascular health information, and early risk detection. Compared to existing systems, the proposed system contributes to an advancement in bilingual digital health literacy, patient education guided by interactive AI, real-time care coordination, and scalable deployment, making a promising step forward in patient-centered cardiovascular care.
Murad Al-Rajab, Samer Ellahham, Mohammad Omar Qassem et al.· Scientific Reports· 0 citations
As artificial intelligence (AI) grows more sophisticated in the modern era, transnational commercial trade involving AI-generated content has become increasingly frequent, giving rise to complex intellectual property frictions among multinational enterprises. This essay focuses on the intellectual property dispute resolution mechanism under the framework of the "Agreement on Trade-Related Aspects of Intellectual Property Rights" (TRIPS), and its applicability in the commercial application of AI. An extraterritorial study is conducted by taking the United States and the European Union as examples. Through case analysis, comparative research, and literature analysis, this study explores the current status and limitations of the dispute resolution mechanism among countries under the TRIPS framework, as well as the practical situation of international commercial arbitration in transnational technical disputes. The research finds that although the TRIPS agreement provides a fundamental multilateral legal framework, in resolving issues which are connected with works generated by artificial intelligence, the existing mechanism faces many challenges, including procedural lag, lack of relief measures at the enterprise level, and substantive uncertainty regarding the copyrightability of the output of artificial intelligence. It is necessary to enhance the flexibility of international commercial arbitration at the enterprise level and promote the modernization of TRIPS provisions to adapt to the technological reality of artificial intelligence.
Zhuohong Wu· Lecture Notes in Education P...· 0 citations
The use of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity, especially for creating Intrusion Detection Systems (IDSs), has become increasingly important. These systems are essential for detecting malicious behaviour, identifying network issues, and stopping cyberattacks in real time. Despite extensive research on various ML and Deep Learning (DL) models for IDS, the current literature remains incomplete. It has many different datasets, methods, and evaluation standards. As cyber threats become more advanced, it is crucial to conduct a thorough analysis of ML techniques for intrusion detection. The goal of this Systematic Literature Review (SLR) is to provide a full picture of the most recent academic articles on ML-based IDS. The study addresses important research questions about the most widely used algorithms, the types of attacks and network environments covered, the methodological problems that remain unsolved, and the new trends that should shape future research. Following the PRISMA framework, we conducted a systematic review of peer-reviewed articles published between January 2022 and May 2025. We searched IEEE Xplore, ACM Digital Library, and SpringerLink, yielding 22,558 initial records. After carefully applying strict inclusion criteria, 125 papers were selected for the final analysis. We created a standardised data extraction form (i.e., using MS Excel) to gather bibliographic details, research emphasis, methodological strategies, datasets, evaluation criteria, and recognised constraints. We employed thematic analysis to develop a clear taxonomy. We identified five main research themes in our analysis: (1) ensemble and hybrid learning pipelines focused on performance optimisation (30 papers), (2) context-specific IDS designs for Internet of Things (IoT), cloud, and Software-Defined Networking (SDN) environments (34 papers), (3) data-centric engineering that deals with class imbalance and feature selection (20 papers), (4) deep neural architectures for representation learning (31 papers), and (5) trustworthiness concerns like adversarial robustness, zero-day detection, and Explainable AI (XAI) (10 papers). Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Random Forests are the most commonly used algorithms, often combined. Nonetheless, significant deficiencies remain: about 2% of papers incorporate XAI, only 4% focus on adversarial robustness, and none validate their models in real-world production settings. Denial-of-Service (DoS) and Distributed DoS (DDoS) attacks are the most common types in the literature, whereas Web attacks, ransomware, and advanced persistent threats remain poorly studied. The number of publications grows at an average of 30.2% annually, but the field still relies on legacy benchmark datasets rather than operational validation.
Ali Ahmed, Ramy Mostafa, Mahmoud H. Qutqut et al.· Future Internet· 0 citations
Generative Artificial Intelligence (GenAI) has destabilized conventional assumptions about the evidentiary basis of higher education assessment. When students can produce polished and technically plausible outputs with AI support, final submissions alone may no longer provide sufficient grounds for judging learner reasoning, decision-making, or cognitive ownership. This paper addresses that challenge by developing HAICA (Human-AI Collaborative Assessment) as a conceptual framework for assessment redesign in AI-enabled higher education. HAICA is grounded in constructivism, self-regulated learning, assessment for learning, human-in-the-loop governance, and a socio-technical perspective. The framework is organized around three interrelated mechanisms: Human Ownership, the Human-AI Collaborative Cycle, and Governance Controls which together support reasoning transparency as the intended assessment outcome. The paper makes three contributions. First, it reframes the GenAI challenge in assessment from a problem of detection to one of evidentiary design. Second, it positions reasoning transparency as a central construct for restoring defensible academic judgement in AI-enabled contexts. Third, it translates this framework into explicit design principles and propositions for future empirical testing. An implementation-informed illustration demonstrates the framework’s practical plausibility. The paper argues that defensible assessment in the GenAI era depends on redesigning assessment so that human reasoning remains visible, assessable, and governable.
Jawahir Che Mustapha, Munaisyah Abdullah, Ganesh Kumar· Social Sciences & Humanities...· 0 citations
As generative artificial intelligence (AI) becomes increasingly embedded in emerging adults’ academic, work, and social lives, interactions with AI increasingly involve emotional disclosure and relational engagement beyond instrumental use. However, empirical research has been constrained by the lack of validated instruments capturing behavioral, cognitive, and emotional aspects of human-AI engagement. This study developed and validated a three-scale measurement toolkit using a regionally representative sample of college students from 26 universities in Shanghai (N = 1406, Mage = 18.98, SD = 1.25, 59.7% female). The toolkit includes a Multidimensional AI Use Scale, a Perceived Uniqueness of AI Interaction Scale, and an AI Emotional Attachment Scale. Split-sample exploratory and confirmatory factor analyses supported clear factor structures, satisfactory reliability, and evidence of discriminant and criterion-related validity. Together, these instruments provide a psychometrically sound framework for examining behavioral use, cognitive perceptions, and affective attachment in human-AI engagement among emerging adults.
Chuqi Chen, Huiguang Ren, Junsheng Liu et al.· International Journal of Hum...· 0 citations
The fast-growing generative artificial intelligence industry have brought unprecedented challenges to existing copyright rules, especially regard how developers utilise copyrighted literary, visual and coding works to train neural network models. Legal scholars long debated one core question: does building model weights in the training process constitute a copyrighted reproduction act as define by national copyright laws and international treaties? This paper compare two landmark verdicts released in recent years: the 2024 Ultraman AI copyright dispute judged by Hangzhou Internet Court in China, and Thomson Reuters v. Ross Intelligence ruled by the District Court of Delaware in the United States in 2025. Through case analysis and comparative legal research, this paper sort out different judicial attitudes toward three core technical acts: raw data ingestion, temporary data storage in computing memory, and final weight parameter fixation. The analysis show that Chinese judges adopt an output-centred judging logic, treating temporary storage of copyrighted content during training as an inevitable auxiliary technical step without independent infringement liability. By contrast, American courts conduct a full four-factor fair use test covering every stage of AI training workflow. To balance technological progress and creators' exclusive copyright benefits, this paper put forward a two-tier "market impact balancing test" for courts to judge reproduction disputes arising from AI weight training.
Shenxin Yang· Lecture Notes in Education P...· 0 citations
As artificial intelligence, big data, algorithmic recommendation and short-video technology continue to develop rapidly, the news industry is changing dramatically in the process of digitalisation and will alter its traditional model of news creation and dissemination. This paper explores the current situation of digital technology application in newsrooms, identifies the main problems that have arisen from the changes, and puts forward new ideas for utilising digital technologies in news production and dissemination. Through a systematic review of literature and comparative case studies of top news organisations around the world, it has been found that although digital technology has promoted improvements in production efficiency and expanded the spread of news content, serious problems have also arisen, such as content homogeneity due to algorithmic templates, the formation of information cocoons through personalised recommendations, limited use of technology solely at the distribution stage, and ethical issues caused by artificial intelligence and fake news. Given the circumstances mentioned above, the four directions of innovation proposed in this paper are as follows: A model of human-AI collaborative production; optimised algorithmic distribution with public interest weightings; diversified multi-platform dissemination strategies; and the construction of transparent AI ethics governance frameworks. The above results provide new material for discussions on the future of journalism in the era of generative artificial intelligence and offer some suggestions for news organizations in balancing the efficiency of technology with ethical journalism.
Yan Zhao· Communications in Humanities...· 0 citations
