Search Results (1,385)

Why do humans produce dysphoric dreams and nightmares at all? This paper develops a cautious evolutionary theory of unpleasant dreaming by extending the logic of evolutionary psychiatry to dream life. Research on dream continuity suggests that dreams are not detached from waking mental life, but instead reflect current concerns, emotional preoccupations, memory fragments, and bodily states. Work on threat simulation, emotional processing during sleep, and the neurocognitive structure of nightmares further suggests that some disturbing dreams may not be mere malfunctions. Rather, they may be costly outputs of systems involved in salience tracking, threat rehearsal, emotional adaptation, or flexible model updating. At the same time, nightmare research shows that recurrent and trauma-linked nightmares can become clinically serious, producing sleep disruption, daytime distress, and functional impairment. This paper argues that the proper distinction is not between “useful dreams” and “pathological nightmares” in any simple sense, but between unpleasant dream processes that remain self-limiting and those that cross a threshold into maladaptive feedback loops. On that basis, dream engineering is defended not as routine suppression of unpleasant dreaming, but as a selective and limited intervention appropriate when recurrence, rigidity, carryover, and impairment indicate dysfunction. A structured research agenda is proposed to test this threshold model and to distinguish adaptive bad dreams from pathological nightmares. Full article

Bentonite slurry (BS) and steel slag powder (SS) were co-utilized to develop bentonite-slurry/steel-slag foamed concrete (BS-SSFC). The evolution of compressive strength and the associated deterioration mechanisms were examined after repeated wetting–drying exposure in four environments, namely H₂O, H₂SO₄, NaOH, and Na₂SO₄, by combining mechanical testing with microstructural observations. The mix-design results indicate that, for the SS-only mixtures, 20% SS replacement produced a relatively high strength, whereas the binary SS-BS system reached its maximum strength at 10% SS and 5% BS; this combination was consequently adopted for the durability experiments. After 20 cycles, the severity of degradation followed Na₂SO₄ > H₂SO₄ > NaOH > H₂O. XRD and SEM-EDS evidence shows that sulfate ions in the H₂SO₄ and Na₂SO₄ solutions favored ettringite-type expansive products, and Na₂SO₄ further caused salt-crystallization pressure during drying. For NaOH exposure, the main damage was related to reduced stability of cementitious phases together with ion redistribution and localized re-precipitation in a strongly alkaline pore environment. Based on fractal theory, an empirical strength–degradation correlation model was established by using SEM-derived two-dimensional apparent areal porosity as a structural parameter and by linking fractal dimension with the number of cycles. Within the scope of the present experiments, the model captures the empirical link between strength loss and apparent pore-structure deterioration in BS-SSFC; however, its use remains dependent on the image-acquisition procedure, thresholding method, and material system considered. The results provide useful support for using BS-SSFC in aggressive engineering settings such as saline ground and acid-rain regions. Full article

Contemporary cyber threats increasingly employ multi-stage and behaviourally adaptive strategies that challenge static intrusion detection and non-adaptive deception mechanisms. Existing approaches typically treat threat detection, deception deployment, and adversarial attribution as separate functions, limiting timely response and underusing the behavioural evidence generated during attacker interaction. This study develops and evaluates a theory-informed computational and operational framework for autonomous cyber deception. The principal research artefact is a reusable closed-loop architecture rather than a single predictive model: it specifies the interacting components, interfaces, data and control flows, decision rules, and feedback mechanisms that connect detection, deception, telemetry, and attribution. Methodologically, the study follows an engineering design-and-evaluation approach comprising problem and requirement identification from the literature, architectural synthesis, component-level mathematical modelling, prototype implementation, and controlled cyber-range evaluation. In this context, modelling refers to the distinct computational models embedded within the framework: a hybrid detection model combining supervised classification, anomaly detection, and temporal sequence analysis; a Markov Decision Process and reinforcement-learning policy model for selecting and reconfiguring deception actions under engagement, intelligence-gain, resource, and containment objectives; and similarity-based and Bayesian attribution models for estimating MITRE ATT&CK techniques from incomplete behavioural evidence. The component models were developed offline using the NSL-KDD, CICIDS2017, UNSW-NB15, and ToN-IoT datasets, while the integrated prototype was evaluated separately in a controlled enterprise-like cyber range using reconnaissance, brute-force, exploitation, and multi-stage attack scenarios. The reported classification metrics were calculated from the labelled cyber-range evaluation events, not by pooling the four benchmark datasets. On this integrated cyber-range evaluation set, the system achieved 95.4% detection accuracy, 93.6% precision, 94.7% recall, and a 94.1% F1-score, with a mean detection latency of 85 ms. It also achieved 100% honeypot deployment reliability, 92% dynamic reconfiguration success, 88% fingerprinting resistance, and attacker engagement durations of up to 280 s. The attribution component demonstrated end-to-end generation of ATT&CK-aligned technique hypotheses from deception-derived telemetry; however, the present archived evaluation does not support per-technique or baseline-comparative performance claims. These findings show that specialised models and operational services can be coordinated within a unified adaptive defence process, while also identifying the additional class-level and ablation evidence required for rigorous attribution validation. Full article

Polyoxometalates (POMs) are anionic metal oxide nanoclusters with rich redox chemistry, making them promising candidates for photocatalysis. However, their strong UV-light absorption and rapid charge recombination hinder visible-light applications. This review focuses on covalent organic–inorganic hybridization as a modular strategy to engineer POMs for visible-light photoactivation. By grafting chromophoric ligands, metalloporphyrins, or organometallic complexes onto POM surfaces via robust covalent bonds (e.g., Si–C, P–C, C–C), two key photochemical pathways are enabled: (i) direct visible-light excitation of organic sensitizers followed by intramolecular charge transfer to/from POMs and (ii) modified ligand-to-metal charge transfer (LMCT) transitions in POMs via ligand-induced electronic structure perturbation. We discuss how organic ligands regulate POM frontier orbital energy levels (HOMO/LUMO), redox potentials, and photoresponse range, supported by experimental and density-functional theory (DFT) studies. We also review hybrid systems with organic photosensitizers (e.g., pyrene, boron dipyrromethene (BODIPY)), metalloporphyrins, and organometallic complexes (e.g., Ru(II), Ir(III)), emphasizing structure–activity relationships in electron-transfer efficiency, charge-separation lifetime, and catalytic performance (e.g., hydrogen evolution, selective oxidation). Finally, we outline current challenges and prospects for designing multifunctional POM hybrids with tailored visible-light photocatalytic properties. Full article

Conversion Rate Optimisation (CRO) has emerged as a central strategic priority in e-commerce management, yet its positioning within the broader interactive marketing paradigm remains theoretically underdeveloped. Interactive marketing, defined as a multi-directional value creation process through active customer connection, engagement, participation, and interaction, provides a critical lens through which the effectiveness and efficiency of digital marketing tactics can be understood, as each tactic mediates a distinct form of consumer brand interactivity. The academic literature, however, remains fragmented; no unified comparative framework exists that simultaneously assesses both effectiveness and efficiency of digital marketing tactics within an interactive marketing context. Drawing on the classical effectiveness and efficiency framework and interactive marketing theory, this study addresses this gap through a cross-sectional quantitative survey of 302 digital marketing professionals, evaluating a broad range of tactics across both dimensions using a validated psychometric instrument. The findings reveal that Email Marketing consistently dominates across effectiveness and efficiency assessments, reflecting its permission-based structure and its capacity to sustain ongoing consumer–brand dialogue. Search Engine Marketing exhibits the most pronounced divergence between effectiveness and efficiency, consistent with auction-driven cost dynamics that constrain interactive value creation. Attribution modelling difficulty emerges as the primary structural barrier to CRO implementation, revealing a systemic challenge to evidence-based resource allocation in multi-channel interactive environments. An exploratory factor analysis identifies a three-factor taxonomy of tactic effectiveness, distinguishing Paid Conversion tactics, data-driven optimisation tools, and organic or relationship-based channels, each representing a qualitatively distinct mode of consumer–brand interaction. This study advances interactive marketing theory by providing the first empirically validated effectiveness–efficiency framework for e-commerce CRO, and offers actionable guidance for cross-channel budget allocation decisions in interactive digital environments. Full article

With the rapid advancement of robotic and intelligent technologies, commercially available prosthetic legs can greatly meet the functional capabilities of prosthetic systems for amputees. However, high cost, limited affordability, lack of customization, and the need for tedious and lengthy training for amputees remain critical barriers for the widespread adoption of prosthetic solutions. To address the above concerns in advancing prosthetic technologies, we explore the feasibility of adopting an AI-driven knowledge-engineering method in developing a modular prosthetic system for cost-effectiveness. In comparison with other works we found on the development of prosthetic legs, the innovations of the presented work are as follows: (1) the limitations of available prosthetic legs are discussed based on a thorough background study; (2) it is hypothesized that segmented engineering solutions to overcome identified limitations of available prosthetic legs already exist in other applications, and the challenge is how to identify and integrate them as a holistic system solution; and (3) a new AI-driven knowledge engineering method is proposed where Microsoft 365 Copilot is integrated with axiomatic design theory (ADT) to design a complex product or system. A structured prompting procedure is proposed to utilize a large language model (LLM) in developing the solutions to functional modules in two sequential steps, i.e., qualitative and quantitative reasoning. ADT is applied to decompose a complex system with high-level functional requirements (FRs) into a set of functional modules with sub-FRs when the corresponding engineering solutions can be verified manually by designers. The feasibility and effectiveness of the proposed AI-driven knowledge engineering method are illustrated by developing a conceptual modular prosthetic leg. Full article

The electrocatalytic reduction of CO₂ (CO₂RR) into value-added chemicals represents a promising strategy for achieving carbon-neutral energy conversion. However, it is fundamentally limited by sluggish reaction kinetics, insufficient product selectivity, and the competitive hydrogen evolution reaction (HER). Herein, density functional theory (DFT) calculations were employed to systematically investigate transition-metal single-atom catalysts anchored on boron and nitrogen co-doped graphene (TM@BNG), with the aim of elucidating the role of heteroatom-induced coordination engineering in modulating catalytic performance. The results demonstrate that B, N co-doping effectively tailors the electronic structure of the metal active sites, thereby optimizing the adsorption energetics of key intermediates and dictating the reaction pathways. Among the 27 candidates examined, Pd@BNG, Ag@BNG, Sc@BNG, Cu@BNG, Co@BNG, Cd@BNG, and Y@BNG exhibit superior catalytic activity and selectivity toward CO or HCOOH production, featuring low limiting potentials down to −0.06 V while simultaneously suppressing HER. Mechanistic analysis reveals that product selectivity is governed by the relative stabilization of *COOH and *HCOO intermediates during the initial proton-coupled electron transfer step. Furthermore, a physically interpretable descriptor (φ), derived from intrinsic electronic properties using machine-learning approaches, establishes a volcano-type correlation with the limiting potential and provides an effective activity-screening criterion within the investigated TM@BNG dataset. Collectively, these findings clarify the electronic-structure modulation of TM@BNG single-atom catalysts and provide a system-specific framework for screening related B/N-coordinated CO₂RR electrocatalysts. Full article

The architecture, engineering, and construction (AEC) sector operates increasingly under persistent macroeconomic and supply chain volatility, requiring project organizations to simultaneously achieve delivery efficiency and design innovation. While leadership theory attributes this ambidextrous capability to “integrated leaders” who can enact competing behavioral repertoires in parallel, this assumption remains untested in highly volatile, high-pressure project environments. This study investigates the boundary conditions of integrated leadership theory and examines structurally enabled temporal sequencing as an alternative explanation for organizational ambidexterity. Using an explanatory sequential mixed-methods design in the Turkish AEC sector (Phase 1 Survey: N = 278; Phase 2 Interviews: N = 32), we examined how building information modeling (BIM)-enabled digital governance capabilities and organizational tenure predict organizational ambidexterity, including a moderation analysis examining whether industry volatility conditions the effect of integrated leadership. Phase 1 ordinary least squares (OLS) regression and exploratory machine learning stress testing revealed that self-reported integrated leadership profiles had limited predictive utility for ambidexterity (β = 0.035, p = 0.751) and that industry volatility did not significantly moderate this relationship. Conversely, digital governance capability (β = 0.462, p < 0.001) and organizational tenure (β = 0.205, p < 0.01) emerged as consistent structural predictors even after controlling for firm size, industry volatility, project type, and project duration. Phase 2 qualitative findings help explain this predictive pattern by suggesting that, under persistent volatility, attempts to enact simultaneous behavioral integration may generate cognitive overload and execution friction. Instead, participants described project-level ambidexterity as emerging through temporal sequencing and an alternating focus between exploration and exploitation, supported by digital coordination systems that externalize workflows and relational networks associated with longer organizational tenure. Taken together, the findings suggest structurally enabled temporal sequencing as a theoretically derived, qualitatively supported, and interpretive explanation of how organizational ambidexterity may emerge under persistent volatility, rather than as an empirically established mechanism. Full article

Large language models (LLMs) can assist lesson planning, but simple prompts often yield incomplete and misaligned outputs. This study proposes a three-step prompt framework grounded in Bloom’s taxonomy, Adaptive Control of Thought-Rational (ACT-R) theory, Gagné’s nine events, and problem-chain theory, decomposing planning into objective, unit, and activity design. Using three DeepSeek models (R1, V3, 32B) and five prompting strategies, 150 lesson plans were generated on ten computer networking topics. Coverage of Gagné’s nine events and functional quality were evaluated via an LLM judge and human validation. All theory-based strategies significantly outperformed naive prompting, raising Gagné’s event coverage above 90% in the full corpus and from 74.1% to 89.8–93.5% in human ratings. Functional quality scores improved by up to 17.3% (LLM judge) and 53.6% (human raters). Gagné’s five-stage design outperformed ACT-R’s three-stage design under base conditions, while problem-chain guidance benefited ACT-R substantially. Model capability moderated gains: smaller models benefited most in structural completeness, stronger reasoners achieved higher absolute quality. These findings demonstrate that pedagogically grounded, multi-stage prompts are designed to reconfigure teacher-artificial intelligence (AI) interaction from passive output consumption toward structured collaborative design, offering a scalable intervention for integrating LLMs into instructional workflows. Full article

System models and artifacts continuously require validation and refinement to address imprecise specifications and early-stage requirements in order to derive executable simulations. We propose a multi-layer approach for the automated formalization and execution of partial generative specifications derived from high-level descriptions. The first layer processes model seeds—potentially produced by large language models—that capture coarse structural information such as node relations, ordering, and timing estimates. Rather than requiring fully specified executable models from generative sources, we restrict their role to producing partial specifications, which are then completed through formal synthesis. We implement a synthesis engine based on Boolean satisfiability that constructs executable control flow structures from these partial specifications while enforcing structural consistency and execution semantics. Satisfiability modulo theories are further used to verify temporal properties and establish simulation baselines. The resulting models are then transformed into a set-theoretic discrete event system specification, enabling executable simulation via generated code artifacts. This pipeline establishes a unified pathway from partial generative artifacts to formally validated and executable models. It enables reliable and interpretable exploration of design alternatives and experimentation under formally grounded structural and temporal constraints, while providing a foundation for integrating generative modeling with rigorous execution semantics. Full article

Traditional parameter estimation-based trajectory prediction algorithms for Reentry Glide Vehicles (RGVs) typically suffer from limitations, as they neglect the influence of state variables and rely heavily on high-dimensional motion information. To address these issues, this paper proposes an intelligent trajectory prediction algorithm for RGV via physics-informed constraints and state predictive control. First, based on linear system theory, we derived an analytical expression for the prediction error in parameter estimation methods and demonstrated the superiority of these methods through simulations. Second, the Transformer network based on parallel generative decoding pioneers a parameter estimation method of state predictive control, effectively enhancing the accuracy of medium-to-long-term trajectory prediction for RGVs and resolving the dependency of such methods on high-precision parameter estimation information. Finally, by incorporating physics-informed loss during network training based on the dynamic constraints between state variables and control inputs, the network is transformed into a knowledge-data dual-driven model. Simulation results demonstrate that compared to traditional parameter estimation methods, the proposed method exhibits higher prediction accuracy and improved robustness, offering significant engineering application value. Specifically, for prediction durations ranging from 50 to 200 s, this method keeps the average prediction error and the maximum prediction error within 1.4 km and 3.4 km, respectively. Full article

Closed-loop habitats in space-analogue and other extreme environments must sustain life and performance within tightly bounded constraints on water, energy, food, air revitalisation, waste processing, space, and crew time. Existing assessments emphasise engineering reliability, resource throughput, and human factors, but less often address everyday practices—meals, hygiene, privacy, social rituals, sensory comfort, and care work—as variables relevant to sustainability. This conceptual paper develops closed-loop hospitality as an integrated sustainability assessment framework for analysing how such practices mediate between resource constraints and environmental, social, and operational outcomes. Drawing on sustainability assessment, circular resource use, resilience, socio-technical systems, habitability, and hospitality theory, the framework links constraint variables—including resupply interval, closure ratio, bottlenecks, redundancy, volumetric capacity, crew time scarcity, intercultural crew composition, and health-support capacity—to hospitality design choices and measurable indicators. It identifies outcomes such as resource intensity, recycling and waste burden, system reliability, labour distribution, equity and legitimacy of access, psychosocial wellbeing, compliance, and adaptive resilience. The paper supports threshold setting, scorecards, and protocol design in analogue habitats, arguing that sustainable habitation requires balancing resource stewardship with minimum viable hospitality: preserving dignity, care, privacy, cohesion, and operational continuity within closed-loop limits. Full article

Finite $\alpha$-mixture models are widely used in reliability engineering and survival analysis to model heterogeneous populations arising from multiple latent failure mechanisms. Comparing the reliability characteristics of such models through stochastic orderings is important for system evaluation, maintenance planning, and reliability-based decision making. The modified proportional hazard and reversed hazard rate models generalize the proportional hazard rate and reversed hazard rate models, respectively, and have attracted considerable attention in reliability theory. However, stochastic comparisons of finite $\alpha$-mixture models with MPHR- and MPRHR-distributed components have not been investigated in the existing literature. In this work, we consider two finite $\alpha$-mixture models with modified proportional hazard (reversed hazard) rate components. These mixture models are compared stochastically in the sense of the usual stochastic, hazard rate and reversed hazard rate orders. Sufficient conditions are obtained using vector majorization and chain majorization orders. Examples are provided to illustrate the established results. It is also shown that some of the results based on hazard rate and reversed hazard rate orders can not be extended to the likelihood ratio, relative hazard rate and relative reversed hazard rate orders. The flexibility of the proposed models is validated through aircraft windshield failure-time and active repair time data sets, wherein they successfully identify the underlying heterogeneity structure. Full article

Despite widespread adoption of cybersecurity awareness training (CSAT), a persistent knowledge–behaviour gap continues to undermine organisational security posture, particularly in resource-constrained and developing-country contexts. This 12-week quasi-experimental field study evaluated an AI-adaptive CSAT platform against traditional instructor-led training (ILT) across three Yemeni organisations (total N = 187; AI-Adaptive: n = 94; Control: n = 93). The system used a 4-parameter Bayesian Knowledge Tracing (BKT) engine—with interpretable guess and slip signals—as an auditable pedagogical decision layer that triggered Protection Motivation Theory (PMT) and Theory of Planned Behavior (TPB)-aligned interventions. Extreme baseline imbalances (Cohen’s d > 2.0), at which standard ANCOVA residual adjustment alone is known to be biased and which necessitated advanced causal-inference triangulation, were addressed via a four-method protocol (ANCOVA, Propensity Score Matching, Difference-in-Differences, mixed-effects). All four methods converged on consensus effect sizes of d = 0.66–0.89. IT-verified Tier 2–3 incidents declined by 48.9% (incidence-rate ratio [IRR] = 0.51, 95% CI [0.38, 0.68]); blinded phishing click-rates fell from 8.8% to 2.1% (χ²(1) = 8.74, p = 0.003). Bootstrapped mediation analysis (PROCESS Model 4; 5000 draws) indicated that coping self-efficacy and perceived behavioural control—but not threat appraisal—were jointly associated with 66.4% of the total compliance effect. Rosenbaum bounds Γ = 2.1; E-values ≥ 3.4. The findings are consistent with the hypothesis that AI-adaptive cybersecurity training produces robust, theoretically explicable benefits and that the coping-appraisal pathway, not threat salience, is the active psychological mechanism. The four-method triangulation framework offers a replicable standard for field evaluations with non-random assignment. the consensus envelope d = 0.66–0.89 is the observed range of point estimates across the four estimators; per-method 95% CIs are reported below indirect effect via coping self-efficacy = 0.843 [0.52, 1.19], via PBC = 0.524 [0.28, 0.81], via threat appraisal = 0.059 [−0.07, 0.21] (ns); direct effect c’ = 0.63 (p = 0.026); total effect c = 2.06 [1.58, 2.54]. Full article

Technical and Vocational Education and Training (TVET) is central to Cambodia’s skills-development agenda, but limited evidence explains how students form expectations about public TVET pathways. Grounded in Social Cognitive Career Theory (SCCT), this study examined the factors associated with students’ outcome expectations in public TVET institutions in Phnom Penh, Cambodia. A cross-sectional survey was conducted with 616 bachelor’s-degree students, measuring self-efficacy, perceived social support, perceived government and institutional support, and outcome expectations. Data were analyzed using hierarchical multiple regression and stratified subgroup regressions by sector-based major group. Outcome expectations were positively correlated with all three predictors. Controlling for age and gender, the full model explained 64.9% of the variance in outcome expectations, with perceived government and institutional support showing the strongest association, followed by self-efficacy and perceived social support. In the stratified analyses, self-efficacy and perceived government and institutional support were significant within every sector-based subgroup, whereas perceived social support was significant only in the Engineering/Industrial Technical subgroup. These findings provide associational support for SCCT in a Southeast Asian, lower-middle-income setting and indicate that institutional and government credibility may carry particular weight where a public TVET system is still being established. Full article