Search Results (5,173)

While aspect-based sentiment analysis (ABSA) has made significant progress in the identification of explicit opinion targets, the more challenging case of implicit aspects remains insufficiently studied. Implicit aspect extraction is particularly challenging, as it relies on contextual and semantic cues and requires systems to infer what reviewers mean rather than what they state explicitly. A four-component hybrid pipeline is proposed for explicit and implicit aspect extraction, formulating the task as controlled text generation. The pipeline combines (i) a fine-tuned decoder-only large language model as a generative baseline, (ii) an iterative residual generation strategy that recovers multiple aspects through successive masked generation passes, (iii) paraphrase-based input transformation to broaden the contextual signal, and (iv) domain-specific knowledge graphs activated by linguistic signals to infer implicit aspects. The novelty lies not in the individual components themselves but in their principled orchestration and the linguistically motivated gating logic governing the activation of each stage. Extensive experiments are conducted on eight benchmark ABSA datasets spanning both English and Arabic: SemEval-2014, SemEval-2015, SemEval-2016, ACOS, and M-ABSA for English; and SemEval-2016, HAAD, and M-ABSA for Arabic. The proposed solution outperforms strong baseline methods and recent state-of-the-art models on English datasets, with $F_1$-scores of 0.8533, 0.713, 0.7859, 0.793, and 0.664, respectively. On Arabic datasets, the best-performing configurations achieve $F_1$-scores of 0.7632, 0.4765, and 0.7656 on SemEval-2016, HAAD, and M-ABSA, respectively, with the knowledge-graph component providing consistent and statistically significant gains for implicit aspect identification in both languages. These results demonstrate the effectiveness of generative modeling, iterative generation, paraphrasing, and structured knowledge for aspect extraction and highlight the potential of the proposed approach for implicit aspect identification, in particular for morphologically rich languages such as Arabic, where annotated resources remain scarce. Full article

The present study aims to address critical research gaps in duckweed–microbe symbiotic systems specifically applied to high-load livestock and poultry breeding wastewater. These gaps include the insufficient development of well-characterized, multi-functional, complex microbial consortia adapted to complex livestock wastewater matrices, and the technical challenge of achieving simultaneous efficient wastewater purification and duckweed feed quality enhancement. This study is motivated by the pressing issue of agricultural non-point source pollution, which is caused by large-scale livestock and poultry breeding wastewater discharge, and the high external dependence of the feed industry on protein raw materials. The present study utilised Spirodela as the fundamental material, and a functionally complementary complex symbiotic bacterial consortium consisting of Bacillus subtilis, Bacillus tequilensis and Pseudomonas fluorescens was screened and constructed. An experiment was conducted over a 14-day period in which a range of inoculation ratios were systematically explored. The aim of this experiment was to ascertain the purification efficiency of the duckweed–bacteria symbiotic system on high-load livestock and poultry breeding wastewater. Furthermore, the experiment sought to determine the effect of this purification process on the feed value of duckweed. The results demonstrated that complex bacterial inoculation significantly enhanced wastewater purification efficiency. The final removal rate of ammonia nitrogen in all treatment groups exceeded 90% after 14 days, and the maximum removal rates of total nitrogen and total phosphorus reached 67.0% and 58.9%, respectively, thereby demonstrating superior purification performance in comparison to the control group. The inoculation ratio of 10:1 was identified as the optimal parameter for wastewater purification, while the 5:1 ratio was found to be the maximum for crude protein accumulation in duckweed. The maximum dry-based crude protein content recorded was 38.9% on day 14, representing an increase of 26.3% in comparison with the control group. The established duckweed–bacteria symbiotic system has the capacity to simultaneously achieve the efficient purification of livestock and poultry breeding wastewater and the high-value utilisation of duckweed. The optimal process parameters for a range of application scenarios have been determined. This study contributes to the theoretical framework of aquatic plant–microbe symbiotic remediation and provides technical support for the recycling of wastewater resources and the sustainable development of the livestock and poultry breeding industry. Full article

Artificial intelligence systems that learn or reason from finite empirical data often require uncertainty representations that go beyond a single precise probability distribution. This is especially relevant when observations are scarce, incomplete or not reliable enough to support precise probabilistic assessments. In current data-driven AI tools, empirical information extracted from data must often be converted into a structured uncertainty model before it can be used for reasoning, learning or decision support. The singleton intervals induced by NPI-M and A-NPI-M provide such a representation, since they express the predictive information obtained from the observed data without introducing externally chosen cautiousness parameters. Evidence theory is useful in this context because it allows partial support to be assigned to sets of alternatives, making it suitable for representing imperfect knowledge in AI systems. This paper studies how Non-Parametric Predictive Inference for multinomial data (NPI-M) can be connected with evidence theory through reachable probability intervals. Since the exact NPI-M model does not directly define a credal set, we focus on its approximated version, A-NPI-M, which preserves the NPI-M singleton bounds and represents them through reachable probability intervals. We analyze whether the resulting credal set can be represented exactly by a belief function, showing that this is not possible in general, although exact representations may exist in particular cases. Motivated by this limitation, we construct a basic probability assignment whose belief and plausibility values reproduce the A-NPI-M singleton bounds. The resulting belief function preserves the marginal interval information of A-NPI-M while adding an evidential structure on composite events, and its associated set of compatible probability distributions is included in the A-NPI-M credal set. The construction is presented by cases, illustrated with numerical examples and compared with the belief-function representation of the Imprecise Dirichlet Model. The proposed model provides a theoretical representation layer that may support uncertainty-aware AI procedures by transforming empirical predictive information into structured imperfect knowledge before reasoning, learning or decision-support criteria are applied. Full article

English proficiency is vital for non-native speakers’ career development, yet classroom instruction alone cannot meet practical demands, making informal digital learning of English (IDLE) increasingly important. Artificial intelligence (AI), with conversational and multimodal functions, offers new opportunities for IDLE. However, existing research on AI-mediated IDLE has predominantly focused on language majors and often relied on a single methodological lens, neglecting STEM undergraduates and the complex network dynamics among motivational factors. However, research has largely focused on language majors, leaving STEM majors underexplored. Guided by the Hedonic-Motivation System Adoption Model (HMSAM), this study analyzed data from 413 Chinese STEM majors using partial least squares structural equation modeling (PLS-SEM, SmartPLS 4.0) and psychological network analysis (PNA, R 4.5.3). PLS-SEM results showed that enjoyment was the strongest direct predictor of AI-IDLE, followed by focused immersion, perceived usefulness, and curiosity. Control contributed indirectly via focused immersion, while boredom was non-significant. Perceived ease of use influenced AI-IDLE only through cognitive and emotional pathways. The model explained 58.1% of the variance. PNA further identified enjoyment, focused immersion, and control as central nodes, while the link between perceived usefulness and AI-IDLE was non-significant. These findings suggest that Chinese STEM undergraduates’ AI-IDLE is primarily driven by intrinsic hedonic motivations rather than utilitarian evaluations. The study provides empirical support for designing AI tools that enhance enjoyment and control to foster STEM students’ extracurricular English engagement. Full article

This study presents a reusable adjoint-octree multilevel fast multipole algorithm (MLFMA) for full-wave radar scattering analysis of multi-state unmanned aerial vehicle (UAV) formations. The method is motivated by remote-sensing applications in which dense angular sampling or long motion sequences are required for physically reliable signature generation. Instead of rebuilding a global octree for the full formation at every motion state, the proposed approach assigns each sub-target an independent target-attached local octree that translates and rotates with the rigid body. This preserves mesh–cell affiliation in the body-fixed frame and separates the system operator into a state-invariant intra-target near-field component and a state-dependent inter-target far-field component. Consequently, near-field matrices and sparse approximate inverse preconditioners are assembled once and reused throughout the state sequence, while only inter-target far-field coupling terms are updated. The method is evaluated for six representative UAV formations at 3.5 GHz using monostatic radar cross section (RCS) over a full azimuth sweep. Across all tested formations, the proposed solver reproduces the RCS behavior of conventional MLFMA while substantially reducing computational cost. For Formation A, the center-state total time decreases from 251.4 s to 66.06 s; for Formation C, it decreases from 470.95 s to 76.06 s. Over 100-state sequences, the resulting acceleration reaches approximately 11.8-fold and 15.2-fold, respectively. Jitter-envelope analysis further shows that orientation perturbation produces stronger signature uncertainty than planar displacement. The proposed framework therefore provides an efficient and physically consistent forward solver for radar remote-sensing studies of cooperative UAV formations. Full article

This study developed and evaluated a concept-oriented electricity learning system integrating augmented reality (AR) and non-immersive virtual reality (VR) technologies to support different conceptual learning requirements in the “Basic Electrostatic Phenomena and Electrical Circuits” unit. In the proposed framework, AR supported hands-on circuit construction and visualization of invisible electrical phenomena, whereas non-immersive VR was used for voltage measurement and Ohm’s law experimentation through repeated and controllable exploration. A quasi-experimental design was conducted with 87 ninth-grade students from a public junior high school in Taiwan. Two classes were assigned to the experimental group and two to the control group. The intervention lasted five instructional sessions (225 min). Data were collected using an Electricity Achievement Test and a Science Learning Motivation Questionnaire and analyzed using ANCOVA. The results indicated that the experimental group achieved significantly higher science achievement and learning motivation than the control group. Significant improvements were observed in overall science achievement and across all electricity topics, including basic circuit concepts, voltage and current measurement, and resistance and Ohm’s law concepts. The findings suggest that these learning benefits may be associated with the alignment between technological affordances and conceptual learning requirements. Consistent with the Cognitive Theory of Multimedia Learning, Cognitive Load Theory, and Conceptual Change Theory, the framework may have supported learning through visualization, interaction, experimentation, and conceptual change. This study contributes to educational technology and science education research in two ways. First, it proposes a concept-oriented AR/VR framework that systematically aligns technological affordances with conceptual learning tasks and processing demands in electricity education. Second, it provides empirical evidence for the value of concept-oriented technology integration in supporting science achievement and learning motivation. The findings highlight the importance of aligning technological affordances with conceptual learning requirements when designing technology-enhanced science learning environments. Full article

[d=LE]Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty.Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human-automation interaction. Recent studies suggest that transition performance should not be assessed only through takeover timing or response speed since control resumption quality also depends on traffic complexity, driver readiness, automation limitations, and situational awareness recovery. [d=LE]This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery.This paper motivates and defines a trustworthy companion AI (TCAI) layer for human-aware transition support in automated driving. The TCAI is conceived as a bounded, supervised, and explainable advisory agent that supports the driver without entering the safety-critical vehicle-control loop. It reasons over structured semantic state abstractions derived from a human digital twin, a vehicle digital twin, and a context/road digital twin, exposing driver readiness, automation capability, and contextual urgency in a form that supports traceable, uncertainty-aware, and degradation-aware assistance. [d=LE]Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery.Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, and conversational assistance, we propose a conceptual architecture in which the TCAI coordinates multimodal assistance across different interaction conditions, including vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The companion does not actuate the vehicle; its outputs are constrained by runtime governance, policy enforcement, and deterministic fallback mechanisms. [d=LE]The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.The paper concludes with a research roadmap for validating the proposed architecture under planned transitions, urgent handovers, and degraded or adversarial conditions. Although motivated by SAE Level 3 automation, the approach may also inform fallback-related Level 4 scenarios. Full article

Intrusion detection in industrial cyber-physical systems is constrained by small labelled-attack corpora and by the subtler signal of physical-process attacks compared with classical IT-network intrusions, motivating renewed interest in foundation-model-based detectors; classical detectors are typically trained per dataset and degrade under the distribution shift that is common in operational technology, where attack repertoires evolve faster than retraining cycles. Two foundation-model families are now plausible candidates: open-source Large Language Models (LLMs) and recent tabular foundation models (TabPFN, TabICL) pre-trained for in-context tabular inference. We compare the two families head-to-head, alongside Random Forest and XGBoost classical anchors, across three established industrial security benchmarks (SWaT, HAI, WUSTL-IIoT-2021) under a controlled multi-seed full-holdout protocol with paired McNemar and cross-seed Mann–Whitney tests. The empirical picture is dataset-dependent rather than universal: tabular foundation models establish a strong, previously unreported baseline that is competitive with or superior to classical anchors on every dataset evaluated, while LLMs are complementary detectors with a specific advantage on schemas that carry process-engineering semantics (such as SWaT’s named sensor channels). A per-class analysis on the WUSTL five-class attack taxonomy shows that the two families have structurally different strengths: tabular methods dominate traffic-rich attacks (Denial-of-Service, Reconnaissance), whereas LLMs are competitive on rare attack types (Backdoor, Command Injection). A confidence-gated cascade that escalates only low-confidence tabular decisions to an LLM exceeds either detector alone at a small query budget, and a leave-one-attack-type-out analysis shows that foundation-model detectors generalise to unseen attack families substantially better than the classical anchors. The appropriate detector choice in industrial cyber-physical security is therefore informed by the dataset’s feature schema, the attack-type mix, and the operational cost envelope, rather than by a specific performance metric. Full article

Generative artificial intelligence (GAI) enables students to offload cognitive tasks to an external system, yet the consequences of such cognitive offloading for the development of critical thinking—a core dimension of human intelligence—remain underexplored. Drawing upon cognitive offloading theory and distributed cognition theory, this study investigates the heterogeneity of critical thinking outcomes among Chinese university students who use GAI, focusing on how different patterns of human–AI collaboration relate to cognitive autonomy relinquishment. A questionnaire survey was administered to 353 university students across multiple provinces in China. Cluster analysis and regression analysis were employed to identify distinct user profiles and to examine predictors of critical thinking gains and cognitive autonomy. Four distinct user profiles emerged, ranging from “simple Q&A users” (25.2%) to “critical co-thinkers” (15.6%). Learning motivation was the strongest predictor of both critical thinking gains (β = 0.42) and lower cognitive autonomy relinquishment (β = −0.35). Notably, offloading depth positively predicted cognitive autonomy relinquishment (β = 0.25), revealing a paradoxical pattern: sophisticated GAI use was associated with greater dependence. A “high depth–high dependence” subgroup (25.8%) was identified, disproportionately composed of female students and Information and Communication Technology (ICT) majors. The findings challenge the assumption that deeper GAI engagement automatically yields cognitive benefits. Because all constructs were measured through self-report, the findings are interpreted as reflecting students’ perceptions of their cognitive behaviors and abilities; the methodological implications of this design are discussed in detail. Educational interventions should prioritize metacognitive training over technical skill development to ensure that cognitive offloading enhances rather than undermines critical thinking. Full article

Sustainability transitions confront firms with the following informational paradox: the regulatory pressure to innovate green has intensified, yet the knowledge required to do so is dispersed across agencies, sectors, and jurisdictions that rarely speak to one another. Treating data as a strategic factor of production, this paper asks whether and how opening public data—the systematic release of government-held datasets—reconfigures the conditions under which firms generate green innovation. We model the green-innovation ecosystem as a Complex Adaptive System (CAS) in which heterogeneous, bounded-rational agents co-evolve with a data-mediated selection environment. Within this frame, public data openness (PDO) is not marginal input but an exogenous shock to the fitness landscape that propagates through three coupling channels—supply–demand alignment, recalibration of government intervention, and amplification of green credit. Formal derivations link each channel to a testable proposition, and a multi-period Difference-in-Differences (DIDs) design built on the staggered roll-out of Chinese municipal open-data platforms identifies the causal effects, with Callaway–Sant’Anna estimators and double/debiased machine learning (DDML) addressing recent econometric critiques. The evidence supports each proposition and reveals the following distinctive heterogeneity signature consistent with absorptive-capacity heterogeneity: the policy is most consequential where agents and ecosystems are best able to convert data into knowledge. Reframing PDO as a systemic enabler clarifies why uniform rollouts yield uneven returns and motivates a tiered design that scales with the absorptive capacity of recipient firms and regions. Full article

This study examines user responses to an AI travel assistant in a cross-border tourism service system. Moving beyond adoption-centered technology acceptance research, it conceptualizes these responses as a staged appraisal process in which social and experiential cues shape performance expectancy and effort expectancy, which then influence attitude and two behavioral outcomes: users’ willingness to accept AI and objections to AI. Cosmopolitanism is introduced as an individual-level boundary condition. Survey data were collected from 499 Chinese tourists holding valid South Korean tourist visas after they evaluated Visit Seoul AI, an official AI-based travel-planning tool. The hypotheses were tested using partial least squares structural equation modeling. The results show that social influence, hedonic motivation, and perceived anthropomorphism significantly affect performance expectancy and effort expectancy, which in turn shape attitude. Attitude increases usersf’ willingness to accept AI and reduces objections to AI, with a stronger effect on users’ willingness to accept AI. Cosmopolitanism strengthens the negative effect of hedonic motivation on effort expectancy. This study extends AIDUA to cross-border AI service systems and shows that users may both accept and object to AI travel assistants. Full article

Photobiomodulation (PBM) is a non-invasive therapeutic strategy that uses red and near-infrared (NIR) light in the 590–950 nm range to modulate the cellular and molecular pathways involved in retinal homeostasis. At the molecular level, PBM acts primarily through photon absorption by cytochrome c oxidase (CcO, complex IV of the mitochondrial electron transport chain), whose four metal centres—two copper (CuA and CuB) and two heme groups (heme a and heme a₃)—absorb light across approximately 600–1000 nm. Photon capture promotes photodissociation of inhibitory nitric oxide (NO) from the binuclear CuB–heme a₃ centre, accelerates electron transfer, restores the proton-motive force and increases ATP synthesis. These primary events trigger a coordinated molecular programme that includes (i) transient mitochondrial reactive oxygen species (ROS) bursts that activate the Nrf2/Keap1/ARE axis and upregulate phase II antioxidant enzymes (HO-1, NQO1, GCLC, SOD2, catalase, GPx); (ii) calcium- and cAMP-dependent secondary signalling that converges on PI3K/Akt, MAPK/ERK, AMPK and mTOR pathways; (iii) suppression of NF-κB-driven cytokine production (TNF-α, IL-1β, IL-6) and of NLRP3 inflammasome activation; (iv) downregulation of the HIF-1α/VEGF axis, particularly at 590 nm; (v) anti-apoptotic remodelling of the Bcl-2/Bax ratio with reduced cytochrome c release and caspase-3/9 activation; and (vi) PGC-1α/TFAM/NRF1-driven mitochondrial biogenesis, alongside restoration of fission/fusion homeostasis (Drp1, Mfn1/2, Opa1) and PINK1/Parkin-mediated mitophagy. Wavelength specificity has a defined molecular basis: 590 nm modulates VEGF signalling and RPE pump activity, 660 nm interacts with the CuB centre and enhances O₂ binding at CcO, and 850 nm is absorbed by CuA and supports electron entry into complex IV. A second molecular axis is the bidirectional crosstalk between PBM and the circadian system: mitochondrial respiration, ATP turnover and CcO activity oscillate over the 24 h cycle under the control of the BMAL1/CLOCK and PER/CRY core machinery, the NAD⁺/SIRT1–SIRT3 axis and REV-ERBα. Preliminary preclinical and human observations suggest that NIR-induced bioenergetic and functional gains may be coupled to this rhythm, with greater benefit reported when light is delivered in the morning window (≈08:00–11:00); this time dependence should be regarded as an emerging hypothesis rather than an established clinical principle. The clinical evidence is unevenly developed across indications. It is most robust for non-exudative age-related macular degeneration, where multiwavelength PBM (590/660/850 nm; Valeda Light Delivery System) has shown disease-modifying potential in randomized controlled trials (LIGHTSITE I–III and the LIGHTSITE IIIB extension), with sustained BCVA gains and reduced incidence of geographic atrophy over 24 months and beyond. Evidence for retinitis pigmentosa, central serous chorioretinopathy and, with red-light monotherapy, childhood myopia is at present limited to small or short-term studies and remains preliminary. This narrative review synthesizes the molecular machinery engaged by PBM, integrates clinical findings across retinal diseases and discusses how chronotherapeutic delivery of light, aligned with the molecular clock, may further optimize therapeutic efficacy. Full article

This work presents the kinematic analysis of a redundant planar parallel manipulator within the framework of screw theory. The main contribution of this work is the introduction and kinematic modeling of a novel redundant planar parallel manipulator topology composed exclusively of revolute joints. The proposed architecture is motivated by the search for structurally simple mechanisms with favorable analytical properties for screw-theoretic formulation and potential applications in robotic systems requiring compact and efficient planar motion. For completeness, the displacement analysis is included. Thanks to the simple topology of the otherwise complex mechanism, the inverse–forward displacement problem is resolved through straightforward quadratic equations. The velocity input–output relationship is derived without reliance on passive joint rate velocities, and the acceleration input–output equation is obtained independently of passive joint rate accelerations. These simplifications are achieved by exploiting reciprocal line properties. Numerical examples are provided to illustrate the robustness and effectiveness of the proposed kinematic analysis method across the main topics addressed in this contribution. Full article

This study examines the development and pedagogical impact of an AI-based pedagogical agent designed for modern e-learning environments. The research addresses a key challenge in digital education: the lack of personalization and immediate feedback in traditional e-learning systems. AI-driven agents “support and motivate learners through instructional interaction” and provide adaptive, data-driven learning experiences that surpass the limitations of rule-based systems. The study begins with a systematic literature review following PRISMA 2020, analyzing 46 publications from 2020 to 2025 to identify current AI architectures, pedagogical roles, and the empirical evidence of learning impact. The findings highlight the growing use of machine learning, deep learning, multimodal analytics, and large language models in educational agents. These systems perform roles such as tutor, coach, evaluator, dialogue partner, and consultant, offering cognitive, metacognitive, emotional, and analytical support. Modern agents “continuously monitor user interaction, analyze engagement, and adapt learning content”, enabling highly personalized learning pathways. The study also presents the design of a multimodal pedagogical agent capable of explanation, task generation, diagnostics, and adaptive feedback. Experimental results with students (n = 20) show improved performance, reduced errors, and higher engagement when learning with the agent. Overall, the research demonstrates that AI-based pedagogical agents enhance learning effectiveness and support autonomous learning in higher education. Full article

Most airline passengers and crew assume that the air in the cabin is free from harmful or hazardous substances, as is mandated by airworthiness regulations. While fresh air entering the cabin is sterile (and if recirculated is usually efficiently filtered to remove microorganisms), if the fresh air is bled off the turbine compressors (as is the case in about 95% of airliners currently in service), it may be contaminated with traces of engine oil and ultrafine particles abraded from the turbine blades, and possibly traces of hydraulic fluid leaking from servo systems. Engine oil contains tricresyl phosphate (TCP) as an essential antiwear agent, but it is also a well-known neurotoxin, and it has been suggested that there may be no safe lower limit of exposure, not least because of considerable variation among individuals in sensitivity to tri-ortho-cresyl phosphate (ToCP) and other isomers with at least one ortho constituent. This paper reviews current knowledge about these hazards and discusses the medical and economic motivations for diminishing them. A calculation based on maintaining the life quality index shows that eliminating xenobiotic hazards in aircraft cabin air is likely to be affordable. Full article