Search Results (252)

This paper introduces an iceberg-based digital risk twin (DRT) framework for the health monitoring of complex engineering systems. The proposed model transforms multidimensional sensor and contextual data into a structured, interpretable three-dimensional geometry that captures both observable and latent risk components. Each monitored parameter is represented as a vertical geometric sheet whose height encodes a normalized risk level, producing an evolving iceberg structure in which the visible and submerged regions distinguish emergent anomalies from latent degradation. A formal mathematical formulation is developed, defining the mappings from the risk vector to geometric height functions, spatial layout, and surface composition. The resulting parametric representation provides both analytical tractability and intuitive visualization. A case study involving an aircraft fuel system demonstrates the capacity of the DRT to reveal dominant risk drivers, parameter asymmetries, and temporal trends not easily observable in traditional time-series analysis. The model is shown to integrate naturally into AI-enabled health management pipelines, providing an interpretable intermediary layer between raw data streams and advanced diagnostic or predictive algorithms. Owing to its modular structure and domain-agnostic formulation, the DRT approach is applicable beyond aviation, including power grids, rail systems, and industrial equipment monitoring. The results indicate that the iceberg representation offers a promising foundation for enhancing explainability, situational awareness, and decision support in the monitoring of complex engineering systems. Full article

This study proposes a neural-augmented Libor Market Model (LMM) for swaption surface calibration that enhances expressive power while maintaining the interpretability, arbitrage-free structure, and numerical stability of the classical framework. Classical LMM parametrizations, based on exponential decay volatility functions and static correlation kernels, are known to perform poorly in sparsely quoted and long-tenor regions of swaption volatility cubes. Machine learning–based diffusion models offer flexibility but often lack transparency, stability, and measure-consistent dynamics. To reconcile these requirements, the present approach embeds a compact neural network within the volatility and correlation layers of the LMM, constrained by structural diagnostics, low-rank correlation construction, and HJM-consistent drift. Empirical tests across major currencies (EUR, GBP, USD) and multiple quarterly datasets from 2024 to 2025 show that the neural-augmented LMM consistently outperforms the classical model. Improvements of approximately 7–10% in implied volatility RMSE and 10–15% in PV RMSE are observed across all datasets, with no deterioration in any region of the surface. These results reflect the model’s ability to represent cross-tenor dependencies and surface curvature beyond the reach of classical parametrizations, while remaining economically interpretable and numerically tractable. The findings support hybrid model designs in quantitative finance, where small neural components complement robust analytical structures. The approach aligns with ongoing industry efforts to integrate machine learning into regulatory-compliant pricing models and provides a pathway for future generative LMM variants that retain an arbitrage-free diffusion structure while learning data-driven volatility geometry. Full article

Background/Objectives: Endoscopic variceal ligation (EVL) is a common treatment for preventing variceal bleeding in patients with advanced chronic liver disease (ACLD). However, its acute hemodynamic impact is typically assessed using invasive methods, and there is data on short-term spleen stiffness (SS) dynamics are limited. This pilot study aimed to quantify short-interval changes in liver stiffness (LS) and SS following EVL using transient elastography (TE), and to explore their associations with clinical and laboratory parameters. Methods: This prospective observational study enrolled adults with advanced liver disease undergoing esophagogastroduodenoscopy (EGD) with or without EVL at a tertiary center. Liver and spleen TE were performed in a fasted state immediately before endoscopy and repeated within 12 h after EVL. Organ-specific probes and predefined quality criteria were used, and non-parametric methods were applied to assess within-patient changes and correlations. Results: Fifty patients were included in the study: 21 underwent EVL, while the remaining 29 underwent diagnostic endoscopies only. The most common cause was alcohol-related liver disease. Within the EVL subgroup, the median liver stiffness (LSM) increased from 27.6 kPa to 45.1 kPa, and the median spleen stiffness (SSM) increased from 59.9 kPa to 98.3 kPa, both within 12 h. While these increases showed a uniform direction, they did not reach statistical significance. A higher baseline SS predicted a greater LS increase, and stiffness measures correlated with creatinine, disease duration, Child–Pugh class, albumin and ascites. Conclusions: Short-term increases in liver and spleen stiffness following EVL are consistent with acute hemodynamic alterations, such as increased hepatic perfusion and splenic congestion, rather than structural remodeling. These findings, beyond changes in stiffness alone, support the feasibility of integrating TE, particularly the measurement of SS, into early peri-procedural hemodynamic surveillance after EVL. They also justify larger studies with serial time points and direct portal pressure validation. Full article

Emotional dysregulation (ED) reflects a heightened reactivity to stimuli, characterized by excessive negative affect and impulsive behaviors. This study aimed to evaluate ED in workers seeking care for occupational stress and to examine its associations with sociodemographic characteristics, occupational stress, and the severity of anxiety and depressive symptoms. Eighty-seven workers referred for work-related stress were assessed using the Psychological Stress Measure (PSM) and the Job Content Questionnaire (JCQ) for stress, the Beck Depression Inventory-II (BDI-II) and the Self-Rating Anxiety Scale (SAS) for psychopathology, and the RIPoSt-40 for ED. Group comparisons and correlation analyses were conducted using parametric or non-parametric tests, as appropriate. Forty-six percent of participants met criteria for Adjustment Disorders and 54% for Major Depressive Disorder. No significant differences between diagnostic groups emerged for ED or symptom severity. Women reported higher perceived stress and anxiety than men. Negative ED domains—affective instability, negative emotionality, and emotional impulsivity—showed moderate-to-strong positive correlations with stress, anxiety, and depressive symptoms. Affective instability was also related to job stress dimensions, correlating negatively with decision latitude and positively with job demands. Negative emotional dysregulation appears to be a transdiagnostic vulnerability factor for stress-related psychopathology. Screening for ED may support early detection and targeted preventive interventions in occupational settings. Full article

Background/Objectives: Adults with intellectual disability (ID) experience high rates of psychiatric comorbidity but often face diagnostic challenges and treatment barriers, leading to inappropriate psychotropic medication use. This study examined the extent to which specialized psychiatric assessment and improved diagnostic accuracy had an impact on medication management and clinical outcomes in adults with ID and co-occurring psychiatric disorders. Methods: This observational retrospective study analyzed medical records from 25 adults with ID who underwent specialized psychiatric assessment at a community-based service in Italy between January 2023 and January 2024. Psychopathological diagnoses were established according to Diagnostic Manual—Intellectual Disability, Second Edition (DM-ID2) criteria, based on clinical observation and a comprehensive assessment using validated instruments. Clinical outcomes were assessed using a psychometric tool encompassing multiple psychopathological and behavioral dimensions. Data on psychotropic prescriptions and side effects were also collected. Non-parametric analyses were performed, with significance set at α = 0.05. Results: The proportion of patients with a psychiatric diagnosis increased from 32% to 96% after specialized assessment (p < 0.001), with notable rises in depressive (0% to 32%), bipolar (8% to 36%), anxiety (4% to 24%), and impulse control (0% to 16%) disorders. First-generation antipsychotic prescriptions decreased (from 36% to 8%, p = 0.023), while antidepressant use increased (from 12% to 52%, p = 0.004). The mean number of side effects per patient declined from 1.6 to 0.5 (p < 0.001), particularly the elevated prolactin level and psychomotor retardation. Significant improvements were observed in symptom intensity and frequency across multiple domains, including aggression, mood disturbances, and compulsions (p < 0.001). Conclusions: In this single-center retrospective study, specialized psychiatric assessment was associated with improved diagnostic accuracy, medication management, and clinical outcomes in adults with ID. The increase in psychiatric diagnoses likely reflects improved identification, addressing key challenges in precision diagnosis for people with neurodevelopmental disorders. Although the overall number of prescribed medications remained stable, optimization of treatment regimens reduced first-generation antipsychotic use and related adverse effects. These findings indicates that access to specialized assessment and precision diagnosis could improve psychopharmacological interventions and outcomes for this vulnerable population, but larger, multi-center and longer-term studies are needed to confirm these results. Full article

Background/Objectives: Our aim was to investigate the diagnostic accuracy of prenatal ultrasound (US) in fetal limb abnormalities. As a secondary target, we wanted to correlate various predictors for the diagnosis accuracy. Methods: We prospectively enrolled cases with routine prenatal US performed in five participating centers. Subsequently, we selected and processed all cases with limb abnormalities: suspected, diagnosed, and missed on the prenatal diagnosis scans. We collected data on the type of anomaly, the US equipment and probes used, the operator’s expertise, the gestational age at the diagnosis, the length of the examination, and the use of US reporting form. SPSS 22.0 software was applied to perform the analyses using non-parametric statistical methods. Associations between categorical variables were evaluated using Fisher’s exact test and Chi-square tests. For correlations between the gestational age and the anomaly severity, we used Spearman’s rank-order correlation. Predictive performance of operator- and scan-related variables for diagnostic accuracy was assessed using receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) estimates, standard errors (SE), confidence intervals (95% CI), and p-values reported. Results: Our data showed that most US examinations were performed as part of routine screening (79.7%), and the most frequent anomaly diagnosed was clubfoot. Operators’ expertise demonstrated the highest predictive performance, while technical parameters—scan duration (AUC = 0.20, p = 0.1188) and US equipment (AUC = 0.30, p = 0.3478)—did not significantly predict diagnostic accuracy. Conclusions: The overall diagnostic accuracy of prenatal US was 85.5%. Our findings indicate that diagnostic performance is driven primarily by operator expertise and training, rather than by gestational age at scan and technical parameters. Full article

Bacterial urinary tract infections (UTIs) are common in dogs and cats. This study examined the correlations between routine urinalysis and culture-confirmed infections and described the etiologic agents and antimicrobial susceptibility to support stewardship. In 2023, 1787 urine samples (854 dogs, 933 cats) underwent urinalysis, aerobic culture with species identification, and disk-diffusion testing per Clinical and Laboratory Standards Institute standards; non-parametric statistics with effect sizes were applied. Pyuria was the strongest correlate of infection in both species. Low urine specific gravity was associated with infection and crystal detection, and urine pH correlated weakly with growth in dogs. Nitrite positivity was strongly associated with infection in dogs but showed no diagnostic value in cats. Hematuria showed a weak inverse association in dogs and no association in cats. Females and older animals were more frequently infected, and infections were slightly more common in summer. Most episodes were monomicrobial (85%), predominantly caused by Escherichia coli (48.4% of dogs; 51.5% of cats). E. coli remained broadly susceptible to nitrofurantoin and aminoglycosides. Fluoroquinolone activity was variable. Pseudomonas spp. showed the highest susceptibility to ceftazidime, cefepime, and aminoglycosides. These findings support culture when pyuria, dilute urine, or nitrite positivity is detected and favour short, targeted empiric therapy pending results, guided by a stepwise clinical decision pathway. Full article

Traumatic brain injury (TBI) is a highly heterogeneous disease and achieving an accurate diagnosis remains a significant challenge. Biomarkers play a crucial role in minimizing the reliance on invasive techniques like computed tomography, which also have significant economic costs. Human samples were obtained from prospective cohort studies. Mice were subjected to an experimental model of traumatic brain injury. Biomarker levels, gene expression, and blood–brain barrier integrity were analyzed using ELISA, qRT-PCR, and Evans Blue assay; data were statistically evaluated using parametric or non-parametric tests as appropriate. This study focuses on evaluating the role of matricellular protein thrombospondin-1 (TSP-1) and the tight junction proteins occludin and ZO-1 as potential biomarkers of TBI. We showed that lower serum TSP-1 levels correlated with poor patient outcomes at 6 months compared to those patients with a good outcome. Additionally, the disruption of the blood–brain barrier (BBB) and subsequent release of tight junction proteins allowed us to identify occludin as a potential biomarker for prognosis in a cohort of TBI patients and as a diagnosis biomarker in a subgroup of patients with mild TBI, but its discriminative power as a diagnosis biomarker appears modest, as reflected by an AUC of 0.693. On the other hand, ZO-1 exhibited increased levels but limited diagnostic utility. These findings highlight the critical role of TSP-1 in maintaining BBB integrity and regulating the inflammatory response after a TBI, supported by the worsened condition observed in TSP-1-deficient animals. These results demonstrate the potential of TSP-1 and occludin as valuable biomarkers for secondary injury and disease progression in patients with mild to moderate/severe TBI. Full article

Background/Objectives: Alzheimer’s disease (AD) requires accessible and non-invasive biomarkers that can support early detection, especially in settings lacking specialized expertise. Sonification techniques may offer an alternative way to convey neurophysiological information through auditory perception. This study aimed to evaluate whether human listeners without EEG training can discriminate between sonified electroencephalographic (EEG) patterns from patients with AD and healthy controls. Methods: EEG recordings from 65 subjects (36 with Alzheimer’s, 29 controls) from the Open-Neuro ds004504 dataset were used. Data were processed through sliding-window spectral analysis, extracting relative band powers across five frequency bands (delta: 1–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, beta: 13–30 Hz, gamma: 30–45 Hz) and spectral entropy, aggregated across 10 topographic regions. Extracted features were sonified via parameter mapping to independent synthesis sources per frequency band, implemented in an interactive web interface (Tone.js v14.8.49) enabling auditory evaluation. Eight evaluators without EEG experience blindly classified subjects into two groups based solely on listening to the sonifications. Results: Listeners achieved a mean classification accuracy of 76.12% (SD = 17.95%; range: 49.25–97.01%), exceeding chance performance (p = 0.001, permutation test). Accuracy variability across evaluators suggests that certain auditory cues derived from the sonified features were consistently perceived. Conclusions: Parametric EEG sonification preserves discriminative neurophysiological information that can be perceived through auditory evaluation, enabling above-chance differentiation between Alzheimer’s patients and healthy controls without technical expertise. This proof-of-concept study supports sonification as a complementary, accessible method for examining brain patterns in neurodegenerative diseases and highlight its potential contribution to the development of accessible diagnostic tools. Full article

Artificial Intelligence in Education is expanding rapidly, yet the adaptation of chatbots to specific reading-comprehension levels remains underexplored. This mixed-methods study presents Lectobot, a conversational agent designed to provide personalized scaffolding across three levels of reading comprehension (literal, inferential, and critical). First, we conducted a diagnostic assessment with first-year undergraduates (N = 58) using validated instruments: COMPLECsec (reading comprehension), EMA (Academic Motivation Scale), and MARSI (Metacognitive Strategies). Non-parametric analyses (Kolmogorov–Smirnov; Mann–Whitney U with Benjamini–Hochberg adjustment) indicated wide heterogeneity in comprehension (median global accuracy ≈ 55%) and a predominance of extrinsic motivation, with selective use of problem-solving strategies. These findings informed design rules for Lectobot (text selection, adaptive task difficulty, and strategy prompts). In a five-week implementation with a focus group (n = 8), semi-structured interviews were transcribed and coded in MAXQDA, guided by the Technology Acceptance Model (perceived usefulness and ease of use). Students perceived Lectobot as useful for text understanding and synthesis and moderately easy to use; reported difficulties were mainly technical (access and session continuity), leading to actionable design improvements. We discuss ethical and practical implications for personalized scaffolding in higher education and outline avenues for larger-scale evaluations and broader grade levels. Full article

Background/Objectives: Ocular Allergy (OA) has profound effects on the quality of life (QoL) and ocular health of affected individuals. This study aimed to survey health practitioners in Australia on their knowledge and practices regarding currently available evidence-based diagnostic, treatment, and collaborative care approaches to OA. Methods: The Survey on Ocular Allergy for Health Practitioners (SOAHP), a validated tool, was distributed to various health practitioners across Australia in 2022. The survey data were analysed using descriptive statistics, Fisher’s exact test, and non-parametric tests. Results: A total of 155 participants completed the survey including Allergists/Immunologists (n = 6), General Practitioners (GPs) (n = 29), Ophthalmologists (n = 11), Optometrists (n = 66) and Pharmacists (n = 43). The survey revealed strengths and weakness in health practitioner approaches to OA. In terms of diagnosis, a significant 83.2% of participants were aware that itchy eyes are the hallmark symptom of OA; however, only 67.7% were aware that histamine is what causes the itching. Further to this, 57.4% of participants did not ask about QoL in clinical practice. In terms of management, only 30.3% were aware that some topical allergy eye drops act on eosinophils, and 74.9% were aware of the indications of mast cell stabiliser use. Finally, in terms of collaborative care, 68.4% did not believe there was a clear collaborative care model in Australia. Conclusions: This study revealed patterns in health practitioner approaches to OA. As expected, Ophthalmologists and Optometrists exhibited higher awareness and implementation of evidence-based approaches, compared to GPs and Pharmacists. However, these distinct patterns are likely influenced by differences in training and clinical responsibilities. Nonetheless, all practitioner groups showed gaps in knowledge and evidence-based practices surrounding OA. Thus, educational initiatives are required to ensure best patient-centered care is achieved, with reduced burden on the healthcare system. Full article

Background: Coronary artery disease (CAD) is a multifactorial atherosclerotic disorder. Silicon (Si) is a trace mineral with potential antioxidant, anti-inflammatory, and lipid-modulating effects, but its clinical relevance in cardiovascular disease remains unclear. This study evaluated whether hair Si concentration—reflecting long-term exposure—is associated with CAD severity, clinical phenotype, risk factors, and systemic inflammation. Methods: A total of 130 patients with angiographically confirmed CAD (N = 36, 28% women) who met the inclusion criteria were enrolled. Disease severity was quantified using the Coronary Artery Surgery Study Score (CASSS) and SYNTAX score. Hair Si concentration was determined by inductively coupled plasma optical emission spectrometry (ICP-OES). Associations with demographic, clinical, biochemical, and inflammatory parameters were analyzed using non-parametric tests and multivariable ordinal logistic regression. Results: Median hair Si concentration was 21.3 ppm (range: 0.7–211.0). Hair Si levels showed no significant differences across CAD severity assessed by CASSS (H = 2.51; p = 0.47) or SYNTAX score (r = 0.079; p = 0.37). Similarly, no differences were observed between patients with stable angina and those presenting with acute coronary syndrome (p = 0.57) or between individuals with and without prior myocardial infarction. Hair Si concentration was unrelated to age, BMI, cardiovascular risk factors, lipid profile, or systemic inflammatory indices (all p > 0.2). Conclusions: Hair silicon concentration was not associated with CAD severity, phenotype, or systemic inflammation, suggesting that long-term Si exposure is metabolically neutral in advanced atherosclerosis. Unlike other minerals, silicon appears unlikely to serve as a diagnostic or prognostic biomarker in CAD, although its relevance may be confined to early vascular remodeling and primary prevention. Full article

This study asks whether Ethereum’s proof-of-stake (PoS) incentives not only make economic sense on paper but also feel attractive to real validators who may be loss-averse and sensitive to risk. We take a canonical Eth2 slot-level model of rewards, penalties, costs, and proposer-conditional maximal extractable value (MEV) and overlay a prospect-theoretic valuation that captures reference dependence, loss aversion, diminishing sensitivity, and probability weighting. This Prospect-Theoretic Incentive Mechanism (PT-IM) separates the “money edge” (expected accounting return) from the “felt edge” (behavioral value) by mapping monetary outcomes through a prospect value function and comparing the two across parameter ranges. The mechanism is parametric and modular, allowing different MEV, cost, and penalty profiles to plug in without altering the base PoS model. Using stylized numerical examples, we identify regions where cooperation that pays in expectation can remain unattractive under plausible loss-averse preferences, especially when penalties are salient or MEV is volatile. We discuss how these distortions may affect validator participation, economic security, and the tuning of rewards and penalties in Ethereum’s PoS. Integrating behavioral valuation into crypto-economic design thus provides a practical diagnostic for adjusting protocol parameters when economics and perception diverge. Full article

Background: Retrieval-augmented generation (RAG) aims to reduce hallucinations and outdated knowledge by grounding LLM outputs in retrieved evidence, but empirical results are scattered across tasks, systems, and metrics, limiting cumulative insight. Objective: We aimed to synthesise empirical evidence on RAG effectiveness versus parametric-only baselines, map datasets/architectures/evaluation practices, and surface limitations and research gaps. Methods: This systematic review was conducted and reported in accordance with PRISMA 2020. We searched the ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, and DBLP; all sources were last searched on 13 May 2025. This included studies from January 2020–May 2025 that addressed RAG or similar retrieval-supported systems producing text output, met citation thresholds (≥15 for 2025; ≥30 for 2024 or earlier), and offered original contributions; excluded non-English items, irrelevant works, duplicates, and records without accessible full text. Bias was appraised with a brief checklist; screening used one reviewer with an independent check and discussion. LLM suggestions were advisory only; 2025 citation thresholds were adjusted to limit citation-lag. We used a descriptive approach to synthesise the results, organising studies by themes aligned to RQ1–RQ4 and reporting summary counts/frequencies; no meta-analysis was undertaken due to heterogeneity of designs and metrics. Results: We included 128 studies spanning knowledge-intensive tasks (35/128; 27.3%), open-domain QA (20/128; 15.6%), software engineering (13/128; 10.2%), and medical domains (11/128; 8.6%). Methods have shifted from DPR + seq2seq baselines to modular, policy-driven RAG with hybrid/structure-aware retrieval, uncertainty-triggered loops, memory, and emerging multimodality. Evaluation remains overlap-heavy (EM/$F_1$), with increasing use of retrieval diagnostics (e.g., Recall@k, MRR@k), human judgements, and LLM-as-judge protocols. Efficiency and security (poisoning, leakage, jailbreaks) are growing concerns. Discussion: Evidence supports a shift to modular, policy-driven RAG, combining hybrid/structure-aware retrieval, uncertainty-aware control, memory, and multimodality, to improve grounding and efficiency. To advance from prototypes to dependable systems, we recommend: (i) holistic benchmarks pairing quality with cost/latency and safety, (ii) budget-aware retrieval/tool-use policies, and (iii) provenance-aware pipelines that expose uncertainty and deliver traceable evidence. We note the evidence base may be affected by citation-lag from the inclusion thresholds and by English-only, five-library coverage. Funding: Advanced Research and Engineering Centre. Registration: Not registered. Full article

Background: Post-traumatic stress disorder (PTSD) is a severe psychiatric condition prevalent among combat veterans. Its diagnosis is challenging due to the heterogeneity of clinical presentations and the complex interplay of pathogenic factors. Objective: This study aimed to develop and validate a diagnostic algorithm for combat-related PTSD by integrating clinical data with a panel of biological markers associated with blood–brain barrier disruption (anti-GFAP and anti-NSE antibodies), HPA axis dysfunction (cortisol), and neuroinflammation (IL-6, IL-8). Methods: A total of 721 male participants were enrolled: 434 veterans with PTSD (F43.1), 147 combat veterans without PTSD, and 140 non-combat military controls. All participants underwent clinical and psychometric assessment (Likert scale, HADS). Serum levels of biomarkers were measured using ELISA. Statistical analysis included non-parametric tests, correlation analysis, and binary logistic regression with Wald’s method to build a predictive model. Results: The binary logistic regression model identified cortisol and IL-6 as the most significant predictors of PTSD. The final algorithm, based on a cortisol level below 199.8 nmol/L and an IL-6 level above 0.002438 pg/mL, correctly classified 78% of patients (AUC = 0.724, 95% CI [0.669, 0.779]). Furthermore, levels of IL-4, IL-8, and cortisol positively correlated with the severity of combat stress factors, independent of physical injuries. Conclusions: We developed a novel diagnostic algorithm for combat-related PTSD based on cortisol and IL-6 levels, demonstrating high accuracy. The correlation between neuroinflammatory markers and the severity of combat exposure suggests their role as primary indicators of stress response, highlighting their utility for early risk identification and targeted interventions. Full article