Abstract: The rapid evolution of data-driven fields demands educational paradigms that transition from static analysis to dynamic interaction with live information. This paper presents a novel technical framework, the Dual-Agent Curator-Tutor (DACT), which integrates Artificial Intelligence as a concurrent Real-Time Data Curator and Interactive Tutor within Immersive Analytics (IA) learning environments. The DACT framework features two synergistic AI agents: a Curation Agent that dynamically ingests, filters, and contextualizes live data streams (e.g., IoT, financial feeds) for pedagogical alignment, and a Tutoring Agent that provides adaptive, scaffolded instruction based on multimodal analysis of learner behavior within an immersive visualization space (VR/AR). This creates a closed-loop ecosystem where the data landscape and instructional guidance co-adapt in real-time to the learner’s actions. We detail a modular architecture implementing this model, utilizing perturbation-based learning for adaptive curation—inspired by recent optimization techniques—and a rule-based pedagogical engine. We propose a rigorous quantitative evaluation methodology involving controlled experiments to measure gains in analytical proficiency, cognitive load reduction, and behavioral patterns. The paper argues that this seamless integration of automated data management and personalized tutoring within an immersive context represents a transformative advancement for experiential learning, effectively leveraging technology to offload cognitive overhead and elevate higher-order analytical reasoning skills.

Abstract:

With accelerating climate change and urbanization, river catchments continue to experience structural modifications through dam construction and concrete-lining of natural channels as adaptation measures. These interventions can alter the natural hydrology. This necessitates assessment of their influence on hydrology at a catchment scale. However, such evaluations are particularly challenging in data-scarce regions such as the Chongwe River Catchment, where hydrometric records capturing conditions before and after structural modifications are limited. Therefore, we applied a 2D rain-on-grid approach in HEC-RAS to evaluate changes in high-flow characteristics in the Chongwe River Catchment in Zambia, where structural interventions have been implemented. The terrain was modified in HEC-RAS to represent 21 km of concrete drains and ten dams. Sensitivity analysis was conducted on five model parameters and showed that Manning’s roughness coefficient had by far the largest impact on peak flows. Model calibration and validation showed strong performance with R² = 0.99, NSE = 0.75 and PBIAS = – 0.68 % during calibration and R² = 0.95, NSE = 0.75, PBIAS = – 2.49 % during validation. Four scenarios were simulated to determine the hydrological effects of channel concrete-lining and dams. The results showed that concrete-lining of natural channels in the urban area increased high flows at the main outlet by approximately 4.6%, generated very high channel velocities of up to 20 m/s, increased flood depths by up to 11%, and expanded flood extents by up to 15%. The existing dams reduced peak flows by about 28%, increased lag times, reduced flood depths by about 11%, and reduced flood extents by up to 8% across the catchment. The findings demonstrate that enhancing stormwater conveyance through concrete-lining must be complemented by storage to manage high flows, while future work should explore nature-based solutions to reduce channel velocities and improve sustainable flood mitigation.

Abstract: We present the electroweak sector of Geometric Design (codename GD-313) in a form suitablefor referee audit. The framework selects the 3–13 vacuum Gr(3, 16) via a bounded integer corridordefined by two publicly tabulated anchors. On the dynamical side, we specify an explicit embeddingof SU(2)L °ø U(1)Y into the U(16) structure compatible with the (3, 13) split and computethe induced one-loop coupling ratio from the Grassmannian coset sector. Under clearly statedassumptions (background-field gauge and cancellation of the universal prefactor in the ratio), theindex computation yields sin2 θW = 3/13 at the declared matching convention. We provide anauditable appendices package: embedding generators, index computation, and a reproducibility checklist.

Abstract:

Building on our previous systematic review that synthesized eight core sustainable appetitive traits central to food behavior research, the present study extends this framework through an empirical investigation of Generation Z university students in Greece. We have established the conceptual foundation by mapping emotional, sensory, and behavioral regulation drivers of eating behavior, underscoring their relevance for nutrition and sustainability. However, empirical applications of this multidimensional framework to Generation Z remained scarce. This study addresses this gap by examining eating behaviors among approximately 800 students at the University of Ioannina using a validated post pandemic questionnaire. Results revealed heterogeneity across six domains, with consensus observed only in sensory driven eating (M = 3.88) and openness to new foods (M = 4.00). Cluster analysis identified two distinct profiles: Exploratory and Hedonic Responders and Emotionally Regulated and Satiety Oriented Responders. These clusters delineate a novel profile of Generation Z, portraying them as digitally immersed, sustainability oriented, and emotionally sensitive, yet divided between impulsive exploration and regulated satiety. The study contributes new empirical insights into post pandemic food behavior. It establishes a comprehensive evidence base for designing culturally sensitive wellness programs and targeted nutritional interventions that support sustainable dietary practices. The continuity between the two papers underscores both theoretical importance and the practical necessity of integrating emotional, sensory, and regulatory dimensions in advancing sustainable eating futures among young adults.

Abstract: Traditional reliability engineering paradigms, originally designed to prevent physical component failures, are facing a fundamental crisis when applied to today's soft-ware-intensive and autonomous systems. In critical domains like aerospace, the dom-inant risks no longer stem from the aleatory uncertainty of hardware breakdowns, but from the deep epistemic uncertainty inherent in complex systematic interactions and non-deterministic algorithms. This paper reviews the historical evolution of reliability engineering, tracing the progression through the Statistical, Physics-of-Failure, and Prognostics eras. It argues that while these failure-centric frameworks perfected the management of predictable risks, they are structurally inadequate for the "unknown unknowns" of modern complexity. To address this methodological vacuum, this study advocates for an imperative shift towards a fourth paradigm: the Resilience Era. Grounded in the principles of Safety-II, this approach redefines the engineering objec-tive from simply minimizing failure rates to ensuring mission success and functional endurance under uncertainty. The paper introduces Uncertainty Control (UC) as the strategic successor to Uncertainty Quantification (UQ), proposing that safety must be architected through behavioral constraints rather than prediction alone. Finally, the paper proposes a new professional identity for the practitioner: the system resilience architect, tasked with designing adaptive architectures that ensure safety in an era of incomplete knowledge.

Abstract:

Background: Patient safety is a top priority for healthcare organization leadership worldwide, as approximately one in ten patients experiences an adverse event, and nurses often report that the quality of the care they deliver is poor. Objectives: The present study aim was to examine the impact of work gaslighting on perceived quality of care, patient safety and quiet quitting on nursing staff. Methods: A cross-sectional study was conducted in Greece and data were collected using an online survey during October to November 2025, with 492 nurses. We used the Gaslighting at Work Scale (GWS) and the Quiet Quitting Scale to measure workplace gaslighting and quiet quitting. Perceived quality of care and perceived patient safety were measured with single items, representing the overall assessments in nurses’ unit. Results: Nurses reported low to moderate levels of workplace gaslighting and quiet quitting, as well as almost half of the participants (52.0%, n=256) evaluated the quality of care in their unit as good, and 33.1% (n=163) of nurses perceived patient safety as good. In the univariate comparisons, greater workplace gaslighting was significantly associated with lower odds of reporting perceived quality of care to be good or excellent (OR = 0.650, 95% CI: 0.527–0.803; p < 0.001). This association was still statistically significant in the multivariable model after gender, years of work experience, working in shifts and working in an understaffed department were included (adjusted OR = 0.655; 95% CI: 0.529–0.810; p < 0.001). Workplace gaslighting was also strongly related to perceived patient safety. In the univariate analysis increased workplace gaslighting was associated with decreased odds of good-to-excellent patient safety (OR = 0.553, 95% CI: 0.445–0.686, p < 0.001). This association remained after controlling for the potential confounders (adjusted OR = 0.561, 95% CI: 0.450–0.700, p < 0.001). In the multivariable model, workplace gaslighting was significantly and positively associated with quiet quitting (adjusted beta = 0.224, 95% CI = 0.163 to 0.285, p < 0.001) after adjusted for demographic and work-related characteristics. Conclusions: The present study is the first that highlighted the significant association between workplace gaslighting and the quality and safety of care, as well as nurses’ quiet quitting. A zero-tolerance stance by senior leadership, coupled with the establishment of clear policies and procedures that encourage staff to report such behaviors, is essential to dismantle the barriers created by psychological manipulation.

Abstract: The evolution of cryptocurrencies has progressed through distinct waves, from the digitization of fiat currencies in the 1980s to the emergence of independent currencies such as Bitcoin and its contemporaries, Ethereum and Ripple. While Bitcoin’s predetermined supply model offers a decentralized monetary approach, it has also resulted in extreme volatility, significantly impacting its adoption as a medium of exchange. This paper explores the rise of stablecoins—cryptocurrencies designed to maintain a stable exchange rate with fiat currencies—as a response to the challenges posed by volatility. Despite the availability of whitepapers and extensive marketing, a knowledge gap persists due to inconsistent terminology and ambiguous designations within the cryptocurrency landscape. This study aims to demystify stablecoin designs, highlighting the importance of clear definitions and precise classifications to aid in regulatory discussions and enhance user understanding. Through a comparative analysis of various stablecoin models and their implications for financial markets, we seek to provide a comprehensive overview of the challenges and opportunities that stablecoins present in the context of evolving financial ecosystems.

Abstract: This study investigates the effects of physical activity on serum cortisol levels and phagocytic capacity of the innate immune system in 8 captive bottlenose dolphins. Analysis of paired samples (n=16) revealed a significant increase in cortisol during periods of physical activity (mean increase of 1.27 µg/dL, 122% elevation), accompanied by decreased phagocytosis in granulocytes (92% reduction) and monocytes (52% reduction). Statistical analyses demonstrated consistent negative correlations between cortisol levels and phagocytic function, suggesting that physical activity influences hypothalamic-pituitary-adrenal axis activation and, consequently, innate immune system function. Sex-differentiated responses were observed, with the male showing attenuated cortisol response but maintained monocyte sensitivity. These findings highlight the complex interplay between the neuroendocrine cortisol response and immune function in cetaceans, with important implications for controlled environments management and animal welfare assessment. A multi-method statistical framework incorporating Bayesian analysis, bootstrapping, and traditional approaches ensured robust inference despite limited sample size.

Abstract: Traditional kernel fuzzers rely on coarse-grained coverage metrics that cannot reflect complex microarchitectural behaviors. We present a hardware-assisted fuzzing framework that leverages branch buffer telemetry from modern CPUs (LBR, BTB sampling) to refine fuzzing feedback. A model-based inference algorithm aggregates branch-data patterns to estimate microarchitectural novelty and guides seed prioritization. Experiments on Intel Ice Lake and AMD Zen 3 systems demonstrate 27% improvement in unique path coverage, with 11 newly identified concurrency bugs across filesystem and scheduler subsystems. Compared with coverage-only fuzzing, our method reduces time-to-crash by 46% while keeping overhead below 12%. This work shows microarchitectural-level signals can significantly boost kernel fuzzing’s effectiveness.

Abstract: This study develops an adaptive workflow allocation mechanism for anti-money laundering (AML) operations, aiming to improve the accuracy and efficiency of suspicious-transaction review. A multi-agent simulation platform was constructed to model transaction flows, alert generation, and analyst decision behaviors. The system integrates model-confidence estimation, analyst-fatigue prediction, and real-time workload signals to dynamically route alerts. Experiments were conducted using 27.3 million historical transactions and 186,000 alerts from a large commercial financial dataset. Compared with fixed allocation rules, the adaptive mechanism increased alert-escalation precision from 0.32 to 0.46 and recall from 0.70 to 0.78, while reducing average handling time by 19.4%. The proportion of high-risk alerts processed within the target time window improved by 23.8%. These results demonstrate that workflow optimization can meaningfully enhance AML performance beyond model-level improvements.

Abstract: Background/Objectives: Wilms tumor is the most common pediatric renal malignancy, and delayed or inaccurate diagnosis can significantly affect clinical outcomes. This study aimed to evaluate whether integrating traditional machine-learning and deep-learning models with computed tomography (CT) imaging could improve the accuracy of Wilms tumor detection. Methods: A large CT image dataset consisting of 18,205 kidney scans, including both normal and Wilms tumor cases, collected from publicly available medical sources. Images were preprocessed and resized to standardized dimensions before model training. Four supervised learning approaches: ResNet50, VGG16, XGBoost, and Random Forest, were developed and evaluated. The dataset was split into training (14,055 images) and independent testing (4,150 images) subsets. Model performance was assessed using accuracy, precision, recall, F1-score, and confusion matrix analysis. Results: Among the evaluated models, VGG16 demonstrated superior performance, achieving an accuracy of 99.98%, precision of 99.92%, recall of 100%, and an F1-score of 99.96%, indicating excellent sensitivity and overall classification reliability. The remaining models also performed robustly, with accuracies exceeding 94% and recall values above 90%. Conclusions: These findings suggest that deep-learning-based image classification, particularly using VGG16, can substantially enhance non-invasive detection of Wilms tumor from CT scans. The proposed approach has the potential to support clinical decision-making, reduce diagnostic delays, and improve early detection in pediatric oncology settings.

Abstract: In response to the rapid evolution of the global marketplace, the proliferation of local hipster coffee shops in Malaysia, including in Kelantan, has intensified competition in attracting and retaining customers, particularly among youth who are highly inclined to explore newly established and trend-driven cafés. Accordingly, this study examines the relationships between location preference, food quality, price, and café atmosphere and youth customer satisfaction at local hipster coffee shops in Kelantan. Data were collected from 384 youth respondents aged 15 to 40 years who had visited local hipster cafés in Kelantan through a self-administered questionnaire distributed via Google Forms. The collected data were analyzed using SPSS version 27.0, employing descriptive statistics, reliability analysis using Cronbach’s alpha, Spearman correlation, and multiple linear regression techniques. Although all measurement items demonstrated satisfactory reliability and validity, the normality test indicated that the data were not normally distributed, justifying the use of non-parametric analysis. The correlation results revealed strong and significant relationships between all studied factors and youth customer satisfaction. However, the multiple regression analysis identified café atmosphere as the most dominant factor influencing youth customer satisfaction, followed by location preference, while food quality and price were found to be statistically insignificant when other variables were considered. These findings offer valuable insights for café operators, business planners, and stakeholders in the coffee industry, enhancing customer experience and competitiveness.

Abstract: Mechanical coffee dryers have been widely adopted to reduce weather dependence, improve yield, and stabilize product quality. However, their operation is still energy-intensive and often suboptimal in terms of controlling the temperature, airflow and moisture content of the grains. In parallel, digital twin (DT) technology has emerged to virtually replicate complex processes and enable model-based monitoring, optimization, and control. This article presents a systematic review based on PRISMA on mechanical coffee dryers and their modeling and control strategies and the current and emerging use of digital twins in drying processes, including agricultural and food products with technological analogies to coffee. The results show a large amount of research on mathematical modeling, energy evaluation, and quality evaluation of mechanical coffee drying. Rapidly growing but still predominantly conceptual literature on digital twins for food processing and drying. Finally, only a small convergence between the two fields, with no fully realized digital twin for mechanical coffee dryers having yet been reported. This review found key gaps in the detection, data infrastructure, and development of hybrid physical-informed AI models. Finally, lines of research are proposed for mechanical coffee dryers enabled with digital twins, aimed at energy efficiency, product traceability and quality assurance.

Abstract: Background We hypothesize that galantamine extracted from Narcissus species may have a protective effect against organophosphate-neurotoxicity, with lower environmental impact. ObjectiveIn vitro testing of the neuroprotective effect of these compounds onneuronal models exposed to an organophosphorus insecticide (diazinon), considering its acute neurotoxicity. Materials and methodsCytotoxicity and protective effect of these compounds in concentrations close to toxic (60µg/ml) and therapeutic (12; 6µg/ml) ones were testedin vitrousing the MTT cell viability assay kit. Statistical analysis was used to test whether the differences between the cell viability of the groups were statistically significant. ResultsThe toxicity values of natural galantamine were higher than those of the synthetic one in concentration of 60µg/ml (p=0.002) and comparable inconcentrations of 6 and 12µg/ml(p=0.06 and p=0.5).Highlighting of the neuroprotective effect by assessing cell viability in the case of exposure of the rat hippocampal neurons cell line to the organophosphorus compound in concentrations of 240 µg/mL, with pretreatment with the studied compounds, emphasized comparable values. In case of exposure to lower concentrations of diazinonthe neuroprotective effect of natural galantamine at a concentration of 12μg/ml is higher than that of synthetic galantamine (p=0.03),andlower than that of the synthetic compound in the case of concentrations of 6 and 60μg/ml (p=0.009 and respectively p=0.002,). Conclusions The protective effect offered by galantamine obtained from the N. poeticusextract was superior to the synthetic compound under experimental conditions at a concentration of 12μg/ml,with lower environmental impact.

Abstract: Background: Head and neck squamous cell carcinomas (HNSCC) are aggressive malignancies with high mortality and poor prognosis. Pseudogenes with altered expression during HNSCC progression may serve as potential biomarkers for early detection, prognosis, and therapy selection. Methods: In this report, we analyzed the expression of ANXA2P2 and PA2G4P4 pseudogenes in HNSCC patients using data from The Cancer Genome Atlas. Clinical correlations were assessed through UALCAN and cBioPortal. Functional enrichment was investigated using REACTOME, ESTIMATE, and Gene Set Enrichment Analysis. Statistical analyses were performed with GraphPad Prism 8. Results: Both ANXA2P2 and PA2G4P4 were significantly upregulated in HNSCC compared to normal tissue. Their expression varied based on HPV status and was negatively correlated with overall survival. High pseudogene expression was associated with dysregulated oncogenic pathways, while low expression was linked to distinct immunological profiles. Conclusions: ANXA2P2 and PA2G4P4 exhibit oncogenic properties in HNSCC and hold potential as diagnostic and prognostic biomarkers.

Abstract: We present MMA-DMF as a one-scale deterministic effective-field theory that reinterprets Standard-Model (SM) mass generation and explicitly confronts macrophysical observables. The framework is locked to a single ultraviolet rigidity scale $M \simeq 100~\mathrm{TeV}$ (Table~4) and replaces continuous flavor inputs with discrete geometric charges $q_f$ (Table~23). In this archive-driven interpretation, inertial mass arises from topological friction: a deterministic drag response of the structured scalar vacuum field $\phi$ encoded in the generalized Langevin / strict fluctuation--dissipation closure (Eq.~8). The 125~GeV resonance is treated as a phonon-like breathing mode of vacuum rigidity (App.~7.4); weak processes are described as topological relaxation via $W/Z$ vortices; and the photon is identified as the gapless $U(1)$, zero-winding mode that remains massless while sensing the vacuum through $\phi F_{\mu\nu}F^{\mu\nu}$ couplings. Finally, the strong interaction is treated as Borromean-ring confinement of scalar flux, so that gluons and an independent $SU(3)$ gauge sector are not fundamental inputs but emergent topological bookkeeping (Sec.~2.14). Beyond consolidating the final rectified equations (including the operational effective-density convolution, Eq.~31.20, and $C^2$ smootherstep nuclear gates, Eq.~13.19), we assemble the dated, deduplicated validation suite emphasized by the audit trail. The ``bridge to macro physics'' is a primary target: GNOME network differential-delay timing (including station overburden effects in $\rho_{\mathrm{eff}}$, where a $1~\mathrm{s}$ surface transient can map to a $\sim 20~\mathrm{ms}$ underground blip) and the mandatory negative-chirp ``Sad Trombone'' veto (forensic logs report $\sim 99.8\%$ rejection of anthropogenic false positives), post-merger gravitational-wave echoes in locked Hayward-regularization windows (e.g.\ $\sim 36~\mathrm{ms}$ for GW150914-class ($\sim 60~M_\odot$) remnants; $\sim 135~\mathrm{ms}$ for the $\sim 225~M_\odot$ high-mass anchor class), Earth neutrino tomography with core-versus-mantle tension, the global 21~cm thermal history with irreducible stochastic heating, and a characteristic dip in galaxy--lensing cross-power. Ablation controls collapse the GNOME correlation to $\mathrm{SNR} \approx 0.4$ when $\Delta T_{ij}$ is removed (Table~19). Microphysical anchoring is provided by lattice convergence of the proton soliton mass to $938.27~\mathrm{MeV}$ with no parameter retuning. The core contribution is an explicitly falsifiable, binary Diamond protocol (Science Run~7, Sec.~6.2): failure of any locked criterion refutes the corresponding sector without introducing new free parameters.

Abstract: Attention-deficit/hyperactivity disorder (ADHD) is characterized by delayed cortical maturation and persistent hyperconnectivity, features often attributed to insufficient synaptic pruning during adolescence. Despite these neurodevelopmental observations, the polygenic architecture underlying pruning dysregulation has remained unclear. Here, we re-analyzed the largest ADHD genome-wide association study (GWAS) meta-analysis to date (38,691 cases, 186,843 controls) using complementary approaches: competitive gene- and gene-set testing (MAGMA), stratified LD score regression-based partitioned heritability, and brain tissue-specific transcriptome-wide association studies (S-PrediXcan).Six hypothesis-driven gene sets were examined: two glutamatergic signaling panels (23 and 130 genes), two synaptic pruning panels (38 and 262 genes spanning complement, microglial, guidance cue, cytoskeletal, and activity-dependent mechanisms), and two negative controls (monoaminergic and housekeeping genes). The expanded pruning set consistently showed significant enrichment across methods—competitive p = 8.37 × 10⁻⁴ (Bonferroni-corrected p = 0.005) in MAGMA, highly significant χ² excess in partitioned heritability (Mann–Whitney p ≈ 10⁻¹⁵–¹⁹), and multiple FDR-significant gene-level associations in TWAS—while glutamatergic sets and controls were null.These findings provide the first robust polygenic evidence linking ADHD liability to broad synaptic pruning dysregulation, offering a molecular basis for delayed maturation and hyperconnected network states. By contrast, canonical glutamatergic pathways do not appear to harbor disproportionate common-variant risk. The results reframe ADHD as a disorder of protracted circuit refinement and highlight potential therapeutic targets in pruning-related machinery, with implications for staged interventions that promote appropriate synaptic elimination early and synaptogenesis later in the lifespan.

Abstract: This research answers the knowledge gap regarding the explanation of the quantum jump of the electron. This scientific paper aims to complete Einstein’s research regarding general relativity and attempt to link general relativity to quantum laws.

Abstract: Energy security and stability are related to the national economy and people's livelihood. Effectively improving the resilience of energy industry supply chain is the key factor to realize energy security and stability. Based on the panel data of 30 provinces in China from 2013 to 2023, this paper empirically explores the influence mechanism of new quality productivity on the resilience of energy industry supply chain. It is found that the new quality productivity can significantly enhance the resilience of the energy industry supply chain. The regulatory effect test results show that the energy supply level can positively promote the new quality productivity and improve the resilience of the energy industry supply chain. The results of threshold effect test show that the level of urbanization development and the degree of industrial synergy agglomeration make the new quality productivity have a nonlinear influence on the resilience of energy industry supply chain. The results of heterogeneity test show that the productivity of new quality plays a more obvious role in promoting the resilience of energy industry supply chain in the central region and the regions with higher information level.

Abstract: Background: Autism spectrum disorder (ASD) affects approximately 1-2% of children worldwide, yet its etiology remains incompletely understood. Emerging evidence suggests that parental autoimmune diseases significantly increase offspring autism risk, with psoriasis (OR 1.59), type 1 diabetes (OR 1.49-2.36), and rheumatoid arthritis (OR 1.51) showing particularly strong associations. Hypothesis: I propose that autism is fundamentally an immune-metabolic disorder characterized by TNF-α-mediated mitochondrial dysfunction leading to cerebral energy deficiency. This energy deficit impairs two critical processes: (1) synaptic pruning during neurodevelopment, and (2) real-time social cognition including gaze processing and emotion recognition. The primary mechanism involves TNF-α pathway dysregulation—through genetic inheritance from parents with autoimmune diseases and/or through direct fetal exposure to elevated maternal TNF-α during pregnancy. Additionally, for cases without parental autoimmune history, I propose a speculative secondary mechanism: mitonuclear immune conflict, where paternal immune genes may partially recognize maternal mitochondria as non-self, generating endogenous TNF-α. Novel Prediction: Based on this framework, I predict that parents with normal-tension glaucoma (NTG)—another TNF-α-mediated condition characterized by neurodegeneration independent of intraocular pressure—will show elevated prevalence of autistic offspring. This prediction has not been tested in any published study. Implications: This hypothesis unifies disparate observations about autism pathophysiology and suggests that anti-inflammatory interventions targeting the TNF-α pathway may have therapeutic potential, particularly when administered early in neurodevelopment.