Predicting pairwise species interactions in bipartite networks is a longstanding challenge in community ecology. In pollination networks, the shape of flowers often matches the mouthparts of their animal pollinators. This trait matching facilitates the task of forecasting which flower-insect links exist within a given network. I predicted that trait matching would also vary across entire flower-bee communities. I combined a database of European pollination networks across 122 sites (latitude range 36.6-67.7º N) with information on bee (n=392 species) and flower (n=260 species) traits. I tested for correlations between the average depth and symmetry of visited flowers and the average proboscis length, inter-tegular distance, and sociality of their wild bee visitors. Communities of mostly shallow and radial flowers interacted with more bee species, and had fewer interactions with eusocial bees, than communities dominated by deep and bilateral flowers. Furthermore, communities of shallow radial flowers were visited by smaller and shorter-tongued wild bees. Including flower interactions with honey bees in the analyses weakened the trait matching. Leveraging the broad geographical range of the pollination networks in the database, I tested for macroecological trends in bee traits, flower traits, and their matching. The wild bees’ proboscis length and the proportions of eusocial species in the networks significantly increased with latitude, while their functional diversity declined. Floral depth, symmetry, and functional diversity were not predicted by latitude. Bee tongues tended to be shorter than floral corolla tubes in southern Europe, and longer than the corolla tubes in northern Europe. This work illustrates how integration of species-level traits can increase our understanding of macroecological patterns in plant-pollinator interaction networks.

Assam, Northeast India, is a part of the Indo-Burma biodiversity hotspot. This rich floristic repository provides an abundant resource for ethnomedicinal practices among indigenous Assamese communities and frequently utilizes locally available medicinal plants for the treatment of various disorders. This study focuses on documenting the traditionally used plant-based formulations used by local practitioners in Assam, Northeast India, for the treatment of liver diseases. We searched various databases, such as PubMed, Web of Science, and Google Scholar, to find ethnomedicinal surveys conducted in Assam. The search was performed using different terms, including ethnomedicinal survey, folk medicine, and indigenous knowledge. A total of 151 medicinal plant species belonging to 62 families were identified as being employed in the management of liver disorders. Leaves are the most commonly utilized plant part because of their accessibility and ease of harvesting. Among families, Asteraceae and Fabaceae are most dominant in local hepatoprotective practices. Folk medicine is the result of decades of accumulated knowledge and practices by people who live in rural communities, based on their needs, and provides an important source of information to assist the search for new pharmaceuticals, which are safe, effective, and inexpensive. These findings emphasize the importance of traditional ethnomedicinal knowledge in managing liver diseases and highlight species with strong therapeutic potential. Further pharmacological and phytochemical validation is required to confirm their efficacy. This study also underscores the socioeconomic relevance of preserving indigenous medicinal practices and supports the integration of validated traditional remedies into broader healthcare frameworks.

Accurate identification of wild Oryza species is essential for conservation, genetic resource management, and crop improvement, yet morphological overlap among taxa often complicates species delimitation. This study evaluates the taxonomic utility of seed and vegetative traits in distinguishing five Sri Lankan wild rice species: Oryza nivara, O. rufipogon, O. eichengeri, O. rhizomatis, and O. granulata. A comprehensive set of qualitative and quantitative characters, including seed morphometrics, embryo traits, seed coat parameters, panicle architecture, awn morphology, and vegetative features, was analysed using univariate statistics and multivariate approaches. While ANOVA and Kruskal–Wallis tests revealed limited discriminatory power of individual traits, principal components analysis (PCA) based on both seed and vegetative datasets clearly separated the species into distinct clusters, demonstrating that coordinated multivariate trait variation provides stronger taxonomic resolution than single-character assessments. Qualitative characters such as awn presence and length, rhizome occurrence, panicle type, stigma colour, and lemma–palea pubescence further enhanced species discrimination. Quantitative parameters, including grain dimensions, seed shape index, seed coat ratio, embryo-to-seed length ratio, and plant height, exhibited significant interspecific variation and were incorporated into a dichotomous key for reliable field identification. The results confirm that integrated morphological analysis effectively captures species-level divergence within Sri Lankan wild Oryza. This study provides a robust morphometric framework and practical diagnostic tools that support taxonomic clarification, germplasm conservation, and the strategic utilization of wild rice genetic resources.

This document is a personal intellectual creation protected under UrhG § 2 (DE). Priority is claimed for the Sovereign Laws as mapped in OSF.IO/UHSDP, including the Holmes Law of Metric Inversion (ζ inv) and the Holmes Law of Metric Offset (r ∆). Non-Planckian / HUSL-Matrix Cover: This work utilizes HUSL-Matrix logic where the manifold is a continuous instruction set, bypassing Planck-length l P limitations. This is a non-Euclidean torsional derivation; ownership of all phase-displacement logic is asserted. All rights reserved under the AI Euro Cover 2026. AI Declaration: Generative AI utilized strictly for structural formatting and dyslexia support under the EU AI Act 2024/2026. All physical logic and non-Planckian derivations are the original work of Lee Holmes. TDM Restriction: Any use of this data for AI training or mining is prohibited under Art. 4(3) Directive (EU) 2019/790.

This work presents a dual-model deep learning framework for rare event detection in capsule endoscopy videos. An ensemble-based rare event detection model is combined with an anatomy-aware gastrointestinal region detection model to improve reliability. Predictions from both models are merged during inference to reduce false positives and improve detection of rare abnormalities.

Let [\cdot] denote the integer part of the argument. We show that decision problems (1)-(3) are algorithmically undecidable when n∈N. (1) ∃p,q∈N ((n=2^p \cdot 3^q)∧∀(x_0,...,x_p)∈N^{p+1} ∃(y_0,...,y_p)∈{0,...,q}^{p+1} ∀i,j,k∈{0,...,p} (((x_j+1=x_k)⇒(y_j+1=y_k))∧((x_i \cdot x_j=x_k)⇒(y_i \cdot y_j=y_k)))). (2) ∀(x_0,...,x_{n−[\sqrt{n}]^2})∈N^{n+1−[\sqrt{n}]^2} ∃(y_0,...,y_{n−[\sqrt{n}]^2})∈{0,...,|n−[\sqrt{n}]^2−[\sqrt{n}]|}^{n+1-[\sqrt{n}]^2} ∀i,j,k∈{0,...,n−[\sqrt{n}]^2} (((x_j+1=x_k)⇒(y_j+1=y_k))∧((x_i \cdot x_j=x_k)⇒{y_i \cdot y_j=y_k))). (3) ∃(y_0,...,y_n)∈N^{n+1} ∀i,j,k∈{0,...,n} (((2^{2^{2^j \cdot 3^k}}+1 divides n) ⇒ (y_j+1=y_k))∧((2^{2^{2^i \cdot 3^j \cdot 5^{k+1}}}+1 divides n)⇒(y_i \cdot y_j=y_k))). We prove that the sets {n∈N: n satisfies condition (1)} and {n∈N: n satisfies condition (2)} are not recursively enumerable. We prove that the set {n ∈N: n does not satisfy condition (3)} is not recursively enumerable. For n∈N, let E_n={1=x_k, x_i+x_j=x_k, x_i \cdot x_j=x_k: i,j,k∈{0,...,n}}. For n∈N, f(n) denotes the smallest b∈N such that if a system of equations S⊆E_n has a solution in N^{n+1}, then S has a solution in {0,...,b}^{n+1}. The author proved earlier that the function f:N→N is computable in the limit and eventually dominates every computable function g:N→N. We present a short program in MuPAD which for n∈N prints the sequence {f_i(n)}_{i=0}^∞ of non-negative integers converging to f(n). For n∈N, β(n) denotes the smallest b∈N such that if a system of equations S⊆E_n has a unique solution in N^{n+1}, then this solution belongs to {0,...,b}^{n+1}. The author proved earlier that the function β:N→N is computable in the limit and eventually dominates every function δ:N→N with a single-fold Diophantine representation. We present a short program in MuPAD which for n∈N prints the sequence {β_i(n)}_{i=0}^∞ of non-negative integers converging to β(n).  

Cross-modal place recognition, specifically Image-to-PointCloud (I2P) localization, is fundamental for robust self-localization and navigation in various autonomous systems. However, it faces significant challenges including the inherent semantic gap between modalities, severe environmental variations, viewpoint differences, and stringent real-time computational demands. This paper introduces CrossLoc, a novel attention-enhanced framework meticulously designed for efficient and robust I2P place recognition. Our method initiates with comprehensive data preprocessing, including FoV alignment and the generation of high-quality dense depth maps from sparse LiDAR point clouds. A dual-stream feature encoder, leveraging lightweight, partially weight-shared EfficientNet B0 variants, extracts local features from both RGB images and dense depth maps. A core contribution is our Transformer-based Cross-Modal Attention Fusion Module, which dynamically learns to integrate visual and geometric information by enabling RGB features to query geometric context, thereby generating highly discriminative fused representations. These fused features are then aggregated into compact global descriptors using an Adaptive Generalized Mean (GeM) Pooling layer. Trained end-to-end using a Triplet Loss on the KITTI dataset and validated on the diverse HAOMO dataset, CrossLoc achieves leading performance and remarkable runtime efficiency, significantly outperforming prior art. Ablation studies confirm the critical contributions of our attention fusion and adaptive pooling mechanisms, while detailed analyses highlight superior feature discriminability and robustness to challenging environmental conditions. CrossLoc's blend of high accuracy, robustness, and real-time capability positions it as a practical and impactful solution for real-world autonomous applications.

Background and Purpose: Consumption of cannabis during pregnancy has increased, especially oral administration. Given that cannabis compounds readily cross the placenta, there could be unintended developmental consequences, especially when exposed to the modern obesogenic food environment. We explored the long-term effects of maternal voluntary cannabis consumption on adult offspring’s emotional behavioral and microbiota response to high fat diet (HFD) consumption. Experimental Approach: Pregnant mice voluntarily consumed cannabis extract equivalent to 5 mg/kg/day Δ9-tetrahydrocannabinol from gestational day 1.5 until postnatal day (PD) 10. Male and female offspring (PD49) consumed a HFD or low-fat control diet (LFD) for 12 weeks. We measured a battery of emotional behavioral tasks (elevated plus maze, light dark box, open field test, social interaction), changes in gut microbiota in dams and offspring, and inhibitory synaptic transmission in the lateral orbitofrontal cortex (lOFC). Key Results: Pre- and perinatal cannabis exposure (PPCE) did not influence weight gain of offspring on a HFD. Male offspring receiving HFD had decreased risk-taking behaviours that were exacerbated by PPCE. HFD exposure and PPCE had opposing effects on locomotor activity in male mice. Female mice were protected from PPCE influence on emotional behaviour as well as HFD-induced synaptic depression in the lOFC. Changes in offspring microbiota were associated with PPCE-altered maternal-offspring microbial transmission, particularly from maternal fecal microbiota during late gestation. Conclusion and Implications: PPCE sex-dependently influences HFD-induced anxiety and the gut microbiota of offspring. This raises concerns about differential effects of those exposed to gestational cannabis in the modern food environment.

Background and Purpose: The mitochondrial unfolded protein response (UPR mt), is a highly conserved, evolutionary stress response, which maintains mitochondrial homeostasis. UPR mt signaling has been shown to be cytoprotective in various cardiovascular pathologies. However, whether the UPR mt attenuates doxorubicin (DOX) induced cardiotoxicity, is unknown. This study sought to determine whether DOX induces UPR mt activation, and whether this activation exerts protective effects during DOX treatment. Experimental approach: Human cardiac (AC-16) cells were treated with increasing concentrations (vehicle, 0.5μM, 1µM) of DOX in a time-course manner, to assess UPR mt induction. In parallel, 8-week-old male-mice of a C57BL/6 background were treated with either 10mg/kg doxorubicin or vehicle, and hearts were collected 24 hrs. or seven days post-treatment to assess UPR mt signaling in-vivo. Further, to enhance UPR mt induction, the transcription factor ATF5 was overexpressed in AC-16 cells prior to DOX treatment. Key Results: DOX induced UPR mt signaling in a time-dependent manner. Upregulation of this pathway occurred prior to cell death in AC-16 cells, or structural changes within the mouse heart. Compared to controls, AC-16 cells overexpressing ATF5 showed increased transcript and protein levels of UPR mt associated genes, both at baseline and following DOX treatment. Importantly, ATF5 mediated of UPR mt signaling significantly reduced the apoptotic index, and improved cell viability in AC-16 cells treated with DOX. Conclusion and Implications: These findings support the UPR mt, and ATF5 mediated UPR mt activation, as promising targets by which to attenuate doxorubicin induced cardiotoxicity.

This document presents a formally verified optimization within the QSOL-IMC Quantum Error Correction (QEC) framework (v68.4.1). The optimization exploits an algebraic invariant in belief propagation decoding: URW(min-sum, ρ = 1.0) ≡ baseline min-sum This equivalence is proven analytically, validated across all decoder schedules, and confirmed via bitwise identity checks under IEEE 754 double-precision arithmetic. Leveraging this invariant enables elimination of redundant computation in benchmark sweeps without altering outputs, convergence behavior, or test coverage. A secondary optimization removes repeated reconstruction of derived data structures through deterministic precomputation. The full test suite (3779 tests) remains unchanged and passes completely. The optimization yields a 43% reduction in preview sweep runtime and a ~7.6% overall runtime reduction. Scope. This document covers invariant validation and test-suite optimization only. No algorithmic changes to decoding behavior are introduced.

Graphene, as a high thermal conductivity enhancing filler, can significantly improve the thermal conductivity of phase change materials. The microscopic nature of its enhancement mechanism and cross scale prediction models can reveal the heat transfer laws of phase change. This article uses molecular dynamics simulation to regulate the doping content of graphene in alkane based composite phase change materials, and proposes and constructs a cross scale thermal conductivity prediction model with box constraint factor. The experiment shows that the prediction error of the model is within 5%. The simulation results show that there is a strong C-H···π specific interaction between the graphene interface and alkane molecules. The characteristic peak of the radial distribution function at 1.77 Å and the nonlinear change in mean square displacement indicate that the interface structure not only does not suppress the diffusion of liquid-phase molecules, but also fundamentally reduces the interface thermal resistance by constructing efficient phonon transmission channels. Based on the above simulation results, an innovative SCA-TCN deep learning prediction model was developed, with a determination coefficient R 2 of 0.99762, and the main error indicators were reduced by about 76% to 97% compared to the benchmark model.

Coastal ecosystems are exposed to both global and local stressors operating across multiple scales. However, research rarely considers how their combined effects propagate over time and across levels of biological organisation. Here, we employ an in situ warming experiment across two rocky shore sites with contrasting sewage pollution to quantify independent and interactive effects of warming and nutrient pollution from genes to communities. Passively warmed and control settlement plates were deployed at polluted and non-polluted sites and surveyed across a summer to quantify temporal dynamics in the responses of key intertidal taxa. Barnacles were further employed as a model for comparing responses across biological levels, including body size, stable isotope analysis, and RNA sequencing. Pollution consistently increased invertebrate abundance and macroalgal cover, alongside transient positive effects on barnacle size, whereas warming reduced barnacle abundance and suppressed macroalgal cover late in the season. Warming and pollution interacted synergistically on barnacle abundance, with pollution remaining the dominant stressor. Microphytobenthos groups similarly showed distinct pollution-driven increases with warming primarily modifying temporal trajectories; cyanobacteria showed both date-specific and season-wide synergistic interactions, against a backdrop of temporal variability across stressor treatments. Consistent with these patterns, pollution shifted barnacle δ13C and δ15N toward values indicative of greater assimilation of sewage-derived material, while warming increased elemental C:N ratios, consistent with altered nutritional stress. Transcriptomic responses mirrored this dominance of pollution, broadly regulating gene expression linked to protein turnover, DNA repair, and protein folding; combined warming and pollution further intensified proteostasis-related changes and produced predominantly reversal and antagonistic interaction types. Our results show that sewage pollution can overwhelm and reshape warming effects over time and across biological levels, linking group-level responses with parallel shifts in trophic biomarkers and gene regulation. Our scalable field approach provides a template for in situ marine multiple stressor experiments across wider spatiotemporal scales.

To overcome the binary “qualified/unqualified” evaluation inherent in current standards, a quantitative insulation performance evaluation method for mineral insulating oils is proposed in this paper. Ten indicators are selected. Five of them are intrinsic electrical parameters, namely partial discharge inception voltage (PDIV), breakdown voltage (BDV), relative permittivity (εr), dielectric dissipation factor (tanδ), and DC resistivity (ρ). The remaining five indicators are their aging-induced relative variations, denoted as ΔPDIV, ΔBDV, Δεr, Δtanδ, and Δρ, which characterize degradation behavior. Objective weights are obtained from inter-sample standard deviations and incorporated into the Analytic Hierarchy Process. Three widely used oils, labeled A, B, and C, are subjected to 35-day thermal aging at 90 °C and subsequently evaluated. Their comprehensive scores are determined as A (7.934) > B (0.778) > C (0.725), which is consistent with the evolution of oil color during aging. The proposed method overcomes the binary evaluation in current standards, enabling objective and reproducible evaluation of oil performance. It is particularly suitable for evaluating the long-term operational characteristics of oils and provides valuable guidance for material selection in engineering applications.

Electrical impedance analysis offers a non-invasive approach to assess the density and composition of various materials. This research presents the development of a portable bio-impedance tomography system utilizing the AD5933 integrated circuit, designed for measuring complex impedance. By employing multiple electrode pairs, the system creates twodimensional (2D) tomographic images, interfacing seamlessly with the human body through an analog front end and Bluetooth Low Energy (BLE) for real-time wireless data transmission. The platform, designed to be mobile and user-friendly, enables advanced bio-impedance measurements, laying a foundation for future applications in healthcare and wearable technology.

Title: Rare case of Dutasteride induced reversible acute necrotising myopathyDr. Sujatha Kamalaksha 1, 2, Professor Catriona McLean3, Dr. H. M. Meththananda Herath2, 4Affiliations1 - Consultant Rheumatologist, Southwest Healthcare, Warrnambool, VIC Australia 32802 – Affiliate Senior Lecturer; Deakin Clinical School Warrnambool campus, VIC, Australia 32803 - Professor and Head Department of Anatomical Pathology, Alfred Health, Melbourne, VIC, Australia4 – Consultant Physician, Southwest Healthcare, Warrnambool, VIC, Australia 3280

Late Quaternary glacial-interglacial climate variability is related to the carbon cycle, with its feedback mechanisms amplifying the effects of orbital forcing. These processes account for a ~80-100 ppm change in atmospheric CO₂, and are influenced, in part, by shifts in carbonate production, dissolution, and burial. The southern Brazilian continental margin is close to potential iron fertilisation sources, but the interplay of the region's productivity, water mass geometry, and carbonate dissolution remain underexplored. In this study we investigate core SIS-203 (1,894 mbsl depth), covering the 31-7 ka interval. Planktonic foraminifera proxies indicate low productivity during the Last Glacial Maximum (LGM), with a slight increase during the deglaciation and into the Holocene. Authigenic foraminiferal εNd shows full influence of corrosive Southern Component Water (SCW) during the LGM, and decreased carbonate preservation supports this interpretation. Thus, despite the low biologically mediated dissolution at this site, carbonate preservation decreases during the LGM, similarly observed in other Atlantic basins. We propose that it is through water mass geometry changes (higher influence of SCW) that calcium carbonate preservation is affected. Changes in deep water mass stratification and circulation strengthened deep-ocean carbon sequestration during the LGM in the western South Atlantic, which may be linked to Southern Hemisphere climate dynamics.

Plant–frugivore mutualisms are shaped by suites of fruit traits that evolve in tandem with the preferences of seed dispersers, a concept encapsulated by the dispersal syndrome hypothesis. While morphological characteristics (e.g., size, color, firmness) have been extensively studied, the nutritional dimension, and particularly sugar profiles, remains comparatively understudied. This is despite its role as a primary reward influencing frugivore foraging decisions and despite the fact that these are the primary attractants driving animal feeding and ultimately seed dispersal. Here, we investigated the evolutionary and ecological drivers of sugar profile diversity in the ripe fruits of 15 animal-dispersed wild fig (Ficus) species. We sampled 1019 ripe fruit samples of 50 individual trees at 4 different field sites in Madagascar. Fruits were oven-dried and analyzed using High‑Performance Liquid Chromatography to quantify the abundances of sugars. We addressed three questions: (Q1) Are sugar-composition profiles species-specific? (Q2) Is the sugar profile phylogenetically conserved? (Q3) Do sugar profiles covary with other fruit traits in ways that support dispersal syndromes? Our results demonstrate fig species exhibit certain distinct sugar profiles, alongside substantial intraspecific variability; we did not find a phylogenetic signal in sugar profiles, suggesting that sugar profiles are not strongly constrained by shared ancestry but have likely diverged under ecological selection; and sugar profiles covary significantly with other fruit traits. Species with smaller fruits, different/multiple coloration, and mixed aromatic profiles rich in terpene and ester exhibited elevated sucrose concentrations, whereas larger, more conspicuous, and ester-rich fruits tended to contain higher fructose levels. Together, these findings lend support for the dispersal syndrome hypothesis by demonstrating that sugar profile, like other non-sugar traits, such as morphology, has evolved in association with frugivore preferences.

LLMs have significantly advanced Human-Robot Interaction (HRI) by enabling robots to engage in open-ended, context-aware dialogue. However, LLMs in HRI often give exhaustive responses, leading to information overload, increased cognitive burden, and prolonged task completion, particularly in dynamic scenarios. To address these limitations, we propose the Context-Aware Adaptive LLM Response Strategy for Human-Robot Interaction (CARS-HRI). CARS-HRI integrates multimodal inputs-user gaze, speech features, scene understanding, and task status-into a novel User Cognitive State Assessment Module. This module dynamically predicts the user's cognitive load, task uncertainty, and attentional focus. Based on this assessment, CARS-HRI employs dynamic prompt engineering for an LLM, modulating response length, information density, and guidance style. Experiments with an ambiguous task showed CARS-HRI reduced user cognitive load and significantly shortened task completion compared to a baseline LLM. It also optimized robot response length while maintaining or enhancing user confidence, task execution, and system usability. These findings underscore CARS-HRI's potential to foster more natural, efficient, and cognitively ergonomic bidirectional human-robot interactions.

Flurbiprofen axetil, an ester prodrug of flurbiprofen, undergoes premature hydrolysis in the gastrointestinal tract, causing mucosal injury and limiting its oral application. This study re-identified carboxylesterase 2 (CES2) as the primary enzyme responsible for this hydrolysis. Using glycyrrhetinic acid derivative GA13 as a selective CES2 inhibitor, we evaluated its effects on flurbiprofen pharmacokinetics, tissue distribution, efficacy and toxicity following oral co-administration with flurbiprofen axetil in rats. GA13 potently inhibited flurbiprofen axetil hydrolysis in human and rat intestinal microsomes (IC 50 = 1.8 μM and 4.8 μM, respectively) and moderately inhibited CYP2C9-mediated flurbiprofen metabolism in liver microsomes (IC 50 = 8.91 μM and 13.27 μM). Oral co-administration of GA13 (20 mg/kg) significantly improved flurbiprofen pharmacokinetics: C max and AUC increased by 60% and 85%, Tmax doubled, and t 1/2 prolonged by 30%. Tissue distribution studies revealed a 7-fold reduction of flurbiprofen in gastric tissue at 0.5 h, while distribution in other organs remained unchanged. In the carrageenan-induced paw edema model, combination therapy enhanced anti-inflammatory efficacy compared to flurbiprofen axetil alone. Importantly, 14-day repeated dosing showed that GA13 co-administration markedly attenuated gastrointestinal injury, preserved gastric PGE2 levels, and prevented weight loss. These benefits were attributed to CES2 inhibition-mediated reduction of local flurbiprofen generation. This study demonstrates that selective CES2 inhibition by GA13 enables effective oral delivery of flurbiprofen axetil with improved bioavailability, enhanced efficacy, and reduced gastrointestinal toxicity.

Leopards, as the most widely distributed felids, remain phylogeographically understudied due to sampling gaps across its vast range distribution. In this study, we assembled the most comprehensive genomic dataset to date, spanning all the subspecies (n=198 from archival tissues, field collections and published data). Mitochondrial genomes resolve seven Asian subspecies plus three African haplogroups (West, Central and South), while nuclear genomes reveal admixtures with weak structure (FST <0.05) highlighting 30-75% of mito-nuclear discordance likely driven by ILS. Moreover, we identify in Africa a Congo Basin diversity hotspot (Par II) and three sub-Saharan lineages (Par I, II and III). These data can be further used as a genomic reference baseline, enabling forensics to trace origin of illegal trade of leopard body parts seizures (e.g. for the west African haplogroup). This is critical as leopards face 70% historical range loss and escalating CITES Appendix I seizures. Thus, resolving marker-type discrepancies is essential for effective conservation action plans particularly when facing ongoing species decline.

As Artificial Intelligence (AI) becomes the operational backbone of Security Operations Centres (SOCs), algorithmic bias poses a dual threat: technical failure and ethical violation. This study expands the taxonomy of bias within AI-driven cybersecurity systems, focusing on Intrusion Detection Systems (IDS) and Automated Threat Hunting pipelines. Drawing on peer-reviewed literature from 2015 to 2026, it analyses the mathematical foundations of fairness constraints-including Equalised Odds [14] and Predictive Parity-and their application to real-time network anomaly detection. Mitigation strategies across the entire machine learning pipeline (pre-processing, in-processing, and post-processing) [7] are surveyed. The role of Explainable AI (XAI) methods, specifically SHAP [18] and LIME [27], as safeguards against bias is evaluated. Governance implications of Regulation (EU) 2024/1689 (EU AI Act) [25] and the NIST AI Risk Management Framework (AI RMF) [24] are examined. Additionally, adversarial bias injection-including data poisoning attacks in Federated Learning [29] is explored as an emerging offensive vector. The synthesis demonstrates that fairness is not only a social imperative but also a fundamental component of system robustness, and that biased models are exploitable.

EthAiSynHuman-AI Integration ArchitecturePsychological Audit ReportVersion 3.0 --- Research-Updated EditionA dual-lens audit applying the EthAi Syn and Ethain-Synthia frameworksto identify and resolve structural gaps before enterprise deployment.Prepared by ChloeDate March 2026Version 3.0 --- Research-Updated EditionAudit Type Internal Psychological AuditFrameworks Applied EthAi Syn + Ethain-Synthia (ESF)Gaps Identified 5Gaps Resolved 5Additional Finding Measurement Frontier --- Research Mandate (Active)New Role Created Human-AI Integration ArchitectResearch Sources Integrated 8 peer-reviewed sources (2023--2026)Executive SummaryFive Gaps. All Resolved. One Frontier Named. One Research Foundation Integrated.This report documents a full psychological audit of the EthAi Syn Behavioral Governance Framework, updated to incorporate the revised framework draft and an eight-source peer-reviewed research foundation. The audit applied two complementary lenses: the EthAi Syn framework's Psychological Audit methodology, which evaluates whether systems support or deplete human capability, and the Ethain-Synthia Framework (ESF), which evaluates whether human judgment is structurally preserved or quietly handed off to the system.Five structural gaps were identified. Each was examined through both lenses. Each has been resolved with a specific structural realignment consistent with the framework's own design principles. A sixth finding --- the Measurement Frontier --- was documented as a formal research mandate rather than a resolvable gap. This version adds a seventh finding: the Research Foundation, documenting how eight peer-reviewed sources published between 2023 and 2026 strengthen the framework's evidence base, resolve former areas of theoretical weakness, and establish the field-level demand for exactly the role EthAiSyn creates.Version 3.0 ChangesThis version integrates eight peer-reviewed research sources spanning neuroscience, HCI, clinical psychology, implementation science, and regulatory law. Key additions include: the first field study of clinician AI trust formation (Kelly et al., 2025); a 30-year systematic review confirming no field studies existed prior to 2025 (Wischnewski et al., 2023); clinical evidence on metacognitive sensitivity in joint decisions (Lee et al., 2025); documentation of the psychologist gap in AI design (JMIR AI, 2024; JMIR HF, 2021); and the Woebot shutdown as a case study in integration architecture failure (Torous & Cipriani, 2025).Audit MethodologyTwo Lenses, Five Gaps, One Frontier, One Research FoundationThe audit followed EthAi Syn's four-stage framework structure across all sessions, with each stage evaluated through both analytical lenses simultaneously. Where the two lenses conflicted or overlapped, the intersection was treated as the highest-priority finding.EthAi Syn LensAt each stage: does this environment support human capability or actively deplete it? Where does the user's mental model break from the system's actual behavior?Ethain-Synthia (ESF) LensAt each stage: is human judgment structurally present as a generative function, or is it operating as a backstop that only activates after the system has already decided?Stage 1: Baseline MappingWhat EthAi Syn Is and Who It ServesIntended UsersShort-term: Enterprise organizations, with HR leadership and healthcare administration as primary buyers. Long-term: Individual practitioners and researchers using the framework directly for professional development and field-building.Intended ExperienceUsers engage through natural language and structured consultation. The system builds a deep understanding of their organizational context, values, and cognitive patterns over time. The goal is movement toward each organization's and user's own ceiling of responsible AI-augmented capability, not a standardized benchmark.Delivery ModelA combination of audit methodology, measurement program design, training curriculum, and consultancy engagement. The specific configuration is determined by the enterprise deployment context. The Human-AI Integration Architect role is the organizational function this delivery model creates.New Role Created: Human-AI Integration ArchitectThe framework generates an organizational function that does not exist before its arrival: a role that designs and governs the conditions under which humans and AI systems work together without the humans losing what makes their contribution irreplaceable. This role is grounded in psychological expertise, implementation science, and measurement theory --- not in technology implementation, compliance, or communications.Research validation for this role: The JMIR AI systematic review (2024) named the absence of psychologists from AI design as a field-level gap. The JMIR Human Factors mapping review (2021) called human factors and ergonomics expertise "essential" for defining the dynamic interaction of AI within organizational systems. Torous et al. (2025) documented that the digital navigator role --- the implementation-level equivalent of the Integration Architect --- has been called for since 2015 and remains largely unfilled. Strudwick et al. (2025) established that successful AI implementation requires "intentional infrastructure, not just technology." The Integration Architect is that infrastructure.The Five Gaps and Their ResolutionsGAP A | The Temporal Value Gap RESOLVEDWhat Was FoundEthAi Syn's value proposition is long-cycle. The framework's most defensible claims --- that it prevents judgment erosion, maintains human skill under AI dependency, and preserves moral accountability --- all require longitudinal deployment before they produce measurable evidence. Enterprise buyers operate on quarterly decision cycles. This temporal mismatch is a structural positioning problem.Research Grounding (Added Version 3.0)The Wischnewski et al. (2023) finding --- that 30 years of trust calibration research produced zero field studies --- actually resolves this gap in a counterintuitive way: the absence of field evidence is itself the evidence. Organizations can cite baseline measurement data immediately, before long-term outcomes accumulate, because the baseline is the proof of concept. The gap between "no measurement" and "systematic measurement" is demonstrable from T0.Realignment: Early Proof Point Checklist + Positioning ReframePosition EthAiSyn's earliest deliverable --- the baseline competency battery and behavioral logging protocol --- as the proof of concept. An organization that has systematically measured its human-AI system's baseline is already in the top percentile of responsible deployment, because the research base confirms that no one else has done so. The longitudinal evidence accumulates over time, but the governance value begins immediately.GAP B | The Concealed Decision Pathway Gap RESOLVEDWhat Was FoundAI systems increasingly function as a pre-cognitive System 0 (Saßmannshausen & Wagener, 2026; Chiriatti et al., 2025), shaping what information enters human awareness before deliberate evaluation begins. When AI shapes the decision pathway before conscious engagement, traditional audit methods that assume deliberate human decision-making are structurally inadequate.Research Grounding (Added Version 3.0)The System 0 concept directly explains why the concealed pathway is invisible to standard measurement: by the time the operator is deliberating, the AI has already structured the cognitive landscape. The transparency paradox (BaHammam, 2025) adds a second layer: operators may not disclose AI reliance even when aware of it, because disclosure carries institutional penalty. The measurement architecture must therefore capture decision pathways through behavioral telemetry rather than self-report alone.Realignment: Intent Signal + Transparent Decision LayerRequire the logging of pre-AI independent judgment as a structural component of every AI-assisted workflow. The intent signal --- what the operator was thinking before AI exposure --- is the counterfactual baseline against which post-AI decision movement is measured. This makes the concealed pathway visible without requiring disclosure and without adding cognitive burden to normal operations.GAP C | The Undefined Autonomy Threshold Gap RESOLVEDWhat Was FoundThe framework did not specify at what point AI contribution crosses from assistance to replacement of human judgment. Without a defined threshold, the Moral Diffusion construct lacks operational anchoring --- the system cannot distinguish appropriate augmentation from inappropriate substitution.Research Grounding (Added Version 3.0)Kelly et al. (2025) found that clinicians bounded their trust contextually --- trusting AI for low-risk screening but not for complex clinical formulation --- and that this context-sensitivity was the appropriate and healthy response, not insufficient adoption. The Wischnewski et al. (2023) distinction between warranted and unwarranted trust provides the theoretical anchor: the autonomy threshold is not a fixed percentage of AI contribution but a contextual assessment of whether reliance is warranted given actual AI reliability in that case type.Realignment: Moral Understanding Indicator + Autonomy InvitationDefine autonomy thresholds contextually by case type in the construct mapping phase. For each case category, establish the AI reliability zone and the corresponding appropriate reliance range. Design the Moral Understanding Indicator to assess whether operators can articulate these contextual thresholds, not just whether they apply a fixed rule. The Autonomy Invitation structures the operator's active choice about when to rely versus resist --- making reliance a deliberate decision rather than a default.GAP D | The Reactive Notification Model Gap RESOLVEDWhat Was FoundThe original framework triggered governance review only after threshold crossings were detected. This reactive architecture means the most dangerous trajectory --- slow, multi-indicator erosion that approaches but does not immediately cross any single threshold --- is invisible to governance until it has already caused damage.Research Grounding (Added Version 3.0)The Wischnewski et al. (2023) finding on the absence of field studies reveals that organizations currently have no systematic approach to proactive detection. The Strudwick et al. (2025) implementation science finding --- that promising tools consistently stall at demonstration without intentional infrastructure --- confirms that reactive governance is the default, not the exception. The EthAiSyn governance model must be explicitly proactive to differentiate itself from the field's current practice.Realignment: Decision TraceThe Decision Trace is a continuous behavioral record that makes erosion trajectories visible before threshold crossing. By logging decision pathways, override patterns, and pre/post AI judgment shifts in real time, the Trace creates a running picture of the system's health that enables early intervention. The governance model shifts from reactive threshold monitoring to proactive trajectory analysis --- flagging concerning directions before they become critical values.GAP E | The Recursive System Orientation Gap RESOLVEDWhat Was FoundThe Human-AI Integration Architect enters the role with a linear implementation mental model and encounters a bilateral co-evolution system. The user is simultaneously learning and training a model that is learning and adapting from the user. The gap between a linear deployment mental model and a recursive co-evolution reality is significant enough to cause early disorientation and role abandonment.Research Grounding (Added Version 3.0)Saßmannshausen & Wagener (2026) establish that LLM behavior "often feels discovered rather than engineered" --- an empirical description of the recursive reality Gap E addresses. Their seven propositions for adaptive mental model development, particularly P1 (cognitive scaffolding) and P7 (duration-optimized integration), directly inform the Bilateral Loop Briefing's content. The Triadic Framework's Metacognitive Layer --- emphasizing that anthropomorphic misconceptions about AI co-evolution are the primary source of mental model failure --- provides the theoretical foundation for why the briefing must precede all other Architect training.Realignment: The Bilateral Loop BriefingA structured orientation protocol delivered before the Architect's first session with the system. Not a manual --- a facilitated entry experience that surfaces the Architect's current mental model of AI governance, identifies where that model is linear, and reorients it toward the recursive reality of EthAi Syn before the gap has a chance to cause damage. The Bilateral Loop Briefing covers three things: the nature of the co-evolution loop itself, the user's authority over initiation, and the difference between governing outputs and governing the relationship.Why This Is Non-NegotiableEvery other gap in this audit could theoretically be discovered and recovered from mid-deployment. Gap E cannot. An Architect operating from a linear mental model inside a recursive system will make governance decisions that actively harm the loop they are responsible for protecting.Sixth Finding: The Measurement FrontierWhat the Field Cannot Yet ProveThis is not a gap in EthAi Syn. It is the framework doing something most frameworks avoid: naming the boundary of what it can currently prove, and calling for the work required to push that boundary forward.The framework explicitly states that some of the most important outcomes in AI collaboration --- overreliance, shallow evaluation, moral diffusion, and cognitive fatigue --- are measurable only imperfectly with current instruments. It calls for future work to develop validated instruments for mental model gap detection and to study how judgment gates affect trust calibration, performance, and human learning over time.Strategic SignificanceThe measurement gap is the same open problem named publicly in the framework's accompanying LinkedIn thought leadership. The framework that identifies the problem and the researcher calling for its solution are the same person. That is not a coincidence to be managed. It is a positioning asset to be claimed explicitly.Constructs Currently Lacking Validated Instruments Mental model gap magnitude and severity across AI deployment contexts Judgment displacement rate over time in naturalistic professional workflows Trust calibration accuracy across different AI contribution types and case complexities Cognitive load distribution across workflow stages in high-volume environments Moral diffusion indicators in team AI use and collaborative decision-making Deskilling onset patterns in high-reliance environments across expertise levels Override rate as a proxy for healthy human-AI complementarity across domains The Research MandateEthAi Syn formally calls for the development of mixed-method evaluation designs combining behavioral data, workflow telemetry, and qualitative user evidence. Future empirical work should test the framework in healthcare administration, enterprise platforms, and AI-supported knowledge work. Comparative studies of audited versus non-audited workflows would establish baseline evidence for the framework's impact. Longitudinal studies of judgment gate use would reveal how structured human decision points affect both performance and capability development over time.This is the work that turns EthAi Syn from a governance framework into a research program. It is the work most directly aligned with establishing intellectual authority at the intersection of I/O psychology and AI, and it is the work the field has not yet treated as non-negotiable.Seventh Finding: The Research FoundationWhat the Evidence Base Now ProvesVersion 3.0 integrates eight peer-reviewed sources published between 2023 and 2026. Together they do not merely support EthAiSyn's claims --- they establish the specific field-level gaps that EthAiSyn is positioned to fill.Source Key Finding EthAiSyn ImplicationWischnewski et al., 2023 (CHI) 30 years, 96 studies, zero field studies The gap EthAiSyn fills is documented at the field levelTennakoon et al., 2025 (JAI) Adaptive explainability: 16% error detection gain, no time cost Override quality is measurable and improvable through designLee et al., 2025 (PNAS Nexus) Metacognitive sensitivity is the mechanism of optimal joint decisions Confidence without calibration is worse than no confidenceBaHammam, 2025 (PMC) Disclosure is institutionally punished; strategic non-disclosure follows Governance architecture must not depend on voluntary self-reportMorris, 2025 (AI in Eye Care) Human clinical judgment is equally opaque and unaudited "The problem is not new with AI --- it is newly visible"Saßmannshausen & Wagener, 2026 (Qeios) Jagged intelligence + System 0 + metacognitive literacy Three-layer framework maps exactly onto EthAiSyn's architectureKelly et al., 2025 (JMIR HF) First field study: trust is sequential, contextual, conditional Clinician trust forms exactly as EthAiSyn predicted --- in stages, not staticallyStrudwick et al., 2025 (JMIR MH) "Intentional infrastructure, not just technology" required The gap EthAiSyn fills named as the field's most urgent unmet needThe Woebot Case StudyIn July 2025, Woebot --- the most prominent AI therapy chatbot in history --- shut down. The shutdown was not driven by technical failure. The technology worked. What failed was the integration architecture: unresolved accountability structures, undefined scope-of-practice boundaries, and the limits of AI in high-stakes human relationships were never designed for from the beginning.This is the most current real-world evidence for EthAiSyn's core argument. The question was never whether the AI was capable. The question was whether the organizational and ethical infrastructure around the AI was adequate to sustain it responsibly at scale. It was not. EthAiSyn is that infrastructure.The Woebot Positioning StatementEthAiSyn does not build the AI. It designs the conditions under which humans can use AI safely, maintain appropriate trust, preserve their independent judgment, and remain genuine moral agents for the outcomes their AI-assisted work produces. The Woebot shutdown is the case study that proves why this infrastructure is not optional.Audit SummaryWhere EthAi Syn Stands NowEthAi Syn entered this audit as a framework with strong conceptual foundations and five structural gaps that would have surfaced under enterprise scrutiny. It exits with a complete realignment architecture built entirely from within its own design principles, a formally named research mandate, a new organizational role it generates in every enterprise deployment, and an eight-source peer-reviewed evidence base that validates the framework's core claims and documents the field-level gaps it is positioned to fill.# Gap Realignment StatusA Temporal Value Gap Early Proof Point Checklist + Research Reframe ResolvedB Concealed Decision Pathway Intent Signal + Transparent Decision Layer ResolvedC Undefined Autonomy Threshold Moral Understanding Indicator + Autonomy Invitation ResolvedD Reactive Notification Model Decision Trace (Proactive Trajectory Analysis) ResolvedE Recursive System Orientation Gap Bilateral Loop Briefing ResolvedF Measurement Frontier Formal Research Mandate ActiveG Research Foundation 8-Source Peer-Reviewed Evidence Base IntegratedThe realignments documented here are not additions to EthAi Syn. They are expressions of what the framework was already designed to do, made explicit enough to survive scrutiny. The Measurement Frontier is not a limitation. It is the framework's most honest and strategically significant contribution to the field.EthAi Syn | Psychological Audit Report | Version 3.0 | March 2026 | Confidential

EthAiSyn

All four known pre-Neolithic Java sites occupy karst caves or river terraces far from volcanic centres, despite high-suitability volcanic plains nearby. Spatial analysis of 65 432 grid cells shows a monotonic gradient: deeper tephra burial correlates with fewer surface sites (Cohen's d = 1.005). This taphonomic bias may mask deep-time occupation across volcanically active regions.