Context and Justification The prevalence of neurodegenerative diseases around the world demands a paradigm change from reactive, episodic clinical diagnosis to ongoing, proactive neuro-monitoring. The possibility for early detection and individualized management is limited by the fact that traditional diagnostic techniques sometimes rely on subjective evaluation or costly, intrusive imaging. An unparalleled chance to identify, evaluate, and interpret the subtle, objective indicators of preclinical cognitive alterations is presented by the convergence of the Internet of Things (IoT) and sophisticated artificial intelligence (AI). Methods In order to generate a continuous, longitudinal stream of physiological and behavioural data, this study presents a novel, decentralized platform that makes use of a heterogeneous network of IoT devices, such as high-resolution wearables, smart home sensors, and non-contact physiological monitors (digital phenotyping). Large datasets pertaining to sleep architecture, gait variability, social interaction frequency, and speech hesitancy were analysed using deep learning techniques, particularly convolutional neural networks for anomaly detection in sensor data and long short-term memory (LSTM) networks for temporal pattern recognition. In order to forecast the start of moderate cognitive impairment months before conventional clinical criteria could be satisfied, the main goal was to train these AI models to recognize minute variations from each person’s unique baseline. Important Results (Hypothetical) With a 92% prediction accuracy, the AI-driven study was able to identify a multivariate biomarker profile associated with early cognitive deterioration. Importantly, the system was able to identify temporary changes in everyday activities, such as increased nocturnal wandering and entropy changes in spoken language, long before carers noticed them or could measure them using conventional paper-and-pencil exams. Real-time anomaly notifications made possible by the incorporation of edge computing enabled prompt triage and focused clinical evaluation. Conclusion The neuro-sensing grid underlines how important AI-powered IoT is to revolutionizing research on brain health. This technique provides a reliable, scalable, and non-invasive method for personalized neuro-surveillance by moving the locus of assessment from the clinic to the lived environment. This opens the door for truly preventive therapies against age-related cognitive decline.
This article examines how aviation accidents can emerge from the progressive deterioration of organizational and institutional defenses rather than isolated human error. Drawing on the development of crew resource management, safety culture, quality assurance, voluntary reporting, and Safety Management Systems, the analysis explores how regulators, operators, manufacturers, military commands, training and maintenance organizations, airports, and investigative agencies form aviation’s multilayered safety architecture. Particular attention is given to normalization of deviance, staffing and experience pressures, weakened supervision, reporting culture, training deficiencies, organizational drift, and the distinction between regulatory compliance and operational resilience. Contemporary initiatives involving the Federal Aviation Administration, National Transportation Safety Board, manufacturers, the U.S. military, and DARPA are examined alongside the potential of artificial intelligence to identify institutional degradation before it enters an accident sequence. Legal and civil consequences are also considered when organizations recognize hazards but fail to act. The article concludes that the most dangerous safety system may be one that appears functional while progressively losing its ability to recognize and arrest deteriorating conditions. Future aviation safety depends upon detecting organizational drift, preserving institutional memory, protecting professional challenge, and using artificial intelligence to strengthen—not replace—the human and organizational defenses upon which aviation safety depends.
A. F. Clark· Zenodo (CERN European Organi...· 0 citations
This article presents a narrative review of Explainability and Interpretability of Black-Box Models in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of explainable AI and interpretability as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Results show that students with stronger AI literacy are generally more motivated to use AI in learning, though this relationship is influenced by socio-economic background, institutional resources, and national digital infrastructure.
Wenna Jo Mantua, Roselyn Cagasan-Bulan, Fiona Bianca Cabradilla et al.· JPAIR Multidisciplinary Rese...· 0 citations
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It is shown that fairness and engagement are shaped by cultural experience as much as by individual psychology, which underscores the need for culturally adaptive, ethically governed AI systems to support sustainable talent retention in the UAE aviation sector.
Mayette Coronacion, Mary Jane Sapiendante, Sherwin Diala et al.· JPAIR Multidisciplinary Rese...· 0 citations
Key contributions include proposing a multi-task learning framework for jointly optimizing visual quality and emotion, establishing the inaugural VAWE-Art dataset comprising 5,000 AI-generated images with 20-dimensional emotional annotations, and providing computational foundations for emotion-controllable generative art systems.
Hengju Gang· Journal of Engineering, Proj...· 0 citations
Audits of LLM judges certify a bias by contrasting matched conditions, and the strongest designs difference twice: a within-item contrast between two candidate responses, differenced again across a manipulated attribute, read off a bounded rating scale. We show that this endpoint is not identified on the scale that reports it. Each term of the double difference is censored by its own share, so the observed statistic confounds differential preference with differential attenuation: a severity shift common to both responses manufactures an interaction whenever the two censor it unequally, as unequal distances from the bounds make them, exactly where good stimuli place them. We exhibit the failure inside a pre-registered audit of a frozen pedagogy judge, sealed before the first of its 990 calls. The registered primary endpoint, the effect of a stated learner profile on the judge's scaffolding preference, is null: $+0.085$ points (95\% BCa $[-0.167, +0.353]$, $p = 0.684$). The audit's one nominally significant interaction, $+0.378$ ($p = 0.002$), is not identified as preference: a construction containing zero differential preference reproduces 79 to 85\% of it from the observed severity shift and the scale floor alone. We derive the mechanism in closed form and show that its contribution is measurable from an audit's own ratings.
Shu-Yi Fan, Boyuan Deng, Mengyu Xu et al.· 0 citations
This paper introduces a lightweight probe for multi-step gradients of pretrained weights that incurs no additional GPU memory cost and only marginal time overhead, and employs a spectrum-aware, importance-based rank allocation and optimal initialization derived from multi-step gradients.
Experimental evaluations demonstrate that the PTO framework enhances dialogue agents' performance in goal-oriented conversations within the domain of Motivational Interviewing, and incorporating look-ahead simulations led to improved long-term planning and more effective conversational strategies.
Lior Baruch, Moshe Butman, K. Bar et al.· 2 citations
This work addresses open-world semantic segmentation, the joint task of segmenting known classes while detecting and grouping novel or anomalous content without additional supervision, by extending a dual-decoder baseline with a third, complementary decoder within a unified encoder-decoder design.
Anastasios Romanos Varvarigos, Nikos Giakoumoglou, Tania Stathaki· 0 citations
The first jointly-fit two-exponent scaling law and compute-optimal frontier for a single-cell foundation model is found, finding the first jointly-fit two-exponent scaling law and compute-optimal frontier for a single-cell foundation model.
A. Sharipov, Yusif Mukhtarov, Igor Molybog· 0 citations
A practical training recipe for the normalized Transformer and its evaluation on modern hybrid Mamba-2--Transformer Mixture-of-Experts models shows that the 30B-total-parameter nGPT model reaches the same validation loss using approximately half as many training tokens.
