Abstract: Office workers are exposed to a range of occupational health risks, including prolonged sedentary behaviour, postural load, elevated heart rate, and noise, yet objective and continuous monitoring of these risk factors in workplace settings remains uncommon. This study aimed to co-design occupational health visualizations based on smartphone and smartwatch data, through a multi-stakeholder group of office workers and occupational health professionals. A generative co-design framework was applied, comprising a pre-design phase with a field study and questionnaire, a structured multi-stakeholder workshop, and a follow-up evaluation session. Thematic analysis of the workshop transcript yielded 17 occupational health themes, which were subsequently assessed for technical feasibility relative to the available sensing platform. Of the 27 discrete visualization elements proposed across both groups, the majority were classified as directly addressable using smartphone and smartwatch sensor data. Visualizations covering physical activity and sedentary behaviour, heart rate, environmental noise exposure, and postural load were implemented in Python using real-world data collected from office workers. The follow-up session confirmed the interpretability and clarity of the developed visualizations. The generative co-design framework proved well-suited to the occupational health visualization context, enabling structured translation of stakeholder requirements into technically feasible and interpretable visualization outputs.

Abstract: A finite hypergraph generalizes an ordinary graph by permitting a hyperedge to connect any nonempty subset of vertices, thereby representing genuine multiway interactions. Extending this idea, a finite SuperHyperGraph is obtained through an iterated powerset construction, so that set-valued objects formed at one level may function as vertices or edge endpoints at the next, providing a natural framework for hierarchical and multilayer relational structures. In contrast, a line graph transforms each edge of a graph into a vertex, with two such new vertices adjacent precisely when the corresponding original edges share an endpoint. In this paper, we introduce the notion of a MultiLine Graph, in which multiple edges can be assigned to a vertex, and then develop its higher-order extensions, namely the MultiLine HyperGraph and the MultiLine Super HyperGraph. We further investigate their fundamental properties and structural characteristics.

Abstract: Structural Health Monitoring (SHM) is essential for the safety and long-term performance of civil and mechanical infrastructure, yet traditional vibration-based approaches often struggle with nonlinear behavior and environmental variability. Koopman operator theory provides a promising alternative by enabling linear analysis of nonlinear structural dynamics through observable functions. This review examines 67 peer-reviewed studies published between 2010 and 2025 and selected using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We outline the development of Koopman-based methods from Dynamic Mode Decomposition (DMD) and Extended-DMD (EDMD) to recent applications in civil, mechanical, and aerospace systems. The review clarifies the mathematical foundations of Koopman analysis and its relationship to structural dynamics. It also identifies major research gaps, including limited damage-sensitive observable design, insufficient use of structural mechanics constraints, the absence of quantitative links between Koopman spectra and physical damage, inadequate benchmarking, and the need for real-time deployment strategies. We conclude by outlining a hybrid Koopman framework that integrates physics-based information with data-driven learning to support interpretable and scalable SHM.

Abstract: Background/Objectives: Patient safety culture represents a holistic approach to ensuring the safety of patient care. When nurses experience abusive behaviors, patient safety culture is undermined. Methods: A cross-sectional study was conducted in Greece, and data were collected via an online survey between October and December 2025. Workplace gaslighting and patient safety culture were measured using the Gaslighting at Work Scale and the Safety Organizing Scale, respectively. Multivariable linear regression analyses were performed using IBM SPSS Statistics 28.0. The level of statistical significance was set at 0.05. Results: The sample included 448 nurses. Almost nine out of ten of the participants (87.3%) were women, with an average age of 38.04 years (SD = 10.27). Regarding educational level, 42.2% held a MSc or PhD degree. Respondents reported moderate perceived workplace gaslighting with a mean score of 2.37 (SD = 1.04) on the Gaslighting at Work Scale. For safety culture, the Safety Organizing Scale yielded a mean score of 5.00 (SD = 0.91). In the univariable analysis, workplace gaslighting was significantly and negatively associated with safety culture (beta = -0.195, 95% CI = -0.275 to -0.115, p < .001), indicating that higher levels of workplace gaslighting were related to worse safety culture behaviors. This association was still significant even when potential confounding variables were considered (adjusted beta = -0.223, 95% CI = -0.305 to -0.142, p < 0.001). Conclusions: This study highlighted the negative impact of workplace gaslighting on patient safety culture. Healthcare organizational leadership is urged to establish and enforce zero-tolerance policies toward gaslighting behaviors and to foster an environment in which nurses are encouraged to speak up openly and report such behaviors.

Abstract: The Phillips curve remains central to monetary policy, yet its functional form has been intensely debated following the 2021–2023 inflation surge. This paper offers novel empirical evidence by providing the first symmetric comparison of regime-dependent nonlinearities in the inflation–slack relationship between the United States and the Euro Area, using identical threshold and smooth-transition frameworks on quarterly data extending through 2025Q4, the most recent available. Core PCE inflation (US) and core HICP excluding energy, food, alcohol, and tobacco (Euro Area) are modeled as functions of unemployment and output gaps, with controls for oil shocks and inflation expectations. TAR/SETAR and LSTAR estimations uncover statistically significant steepening in tight labor-market regimes. In the US, the slope more than doubles when the unemployment gap falls below –0.61 percentage points. In the Euro Area, a comparable kink emerges near zero (–0.048 pp), with smoother transitions reflecting greater wage and price rigidities. Post-2019 subsamples exhibit amplified nonlinearity, consistent with supply-shock transmission in high-pressure conditions. Extensive robustness checks affirm these findings. The results establish a state-dependent sacrifice ratio, with sharply higher disinflation costs in tight regimes, and highlight substantial risks of monetary policy miscalibration in future high-pressure episodes.

Abstract: Predictive climate machine learning is increasingly good at forecasting hazards, but hazard maps alone do not decide what to do, where, when, for whom, and under which futures. We argue that climate ML remains insufficient for adaptation unless interventions are treated as first-class, versioned, and auditable objects. This matters because many climate digital twins still prioritize state estimation and simulation, while adaptation requires intervention observability, counterfactual effect estimation, and constrained portfolio choice. We propose PCA-OS (Planetary Climate Adaptation Operating System), a decision-support operating abstraction that uses an intervention-aware global causal knowledge graph and standardizes object schemas, versioned updates, query primitives, and audit interfaces across three core system objects: (1) an Adaptation Intervention Ledger that records measurable interventions with provenance and uncertainty; (2) a Causal Effect Atlas that stores scenario-indexed, spillover-aware estimands, identification assumptions, diagnostics, and sensitivity bounds; and (3) a Robust Portfolio Decision Layer that optimizes intervention portfolios under budget, equity, and no-harm constraints. We argue that foundation models and intervention-aware world models should support, rather than replace, identification-aware causal analysis by surfacing candidate confounders, mechanisms, and spillover pathways for human review. Finally, we outline AdaptBench, an evaluation suite in which systems can fail for inequitable or maladaptive recommendations even when predictive accuracy is high. The result is a field-level provocation: move climate ML from read-only hazard intelligence to auditable decision support for adaptation.

Abstract: Background/Objectives: Intracerebral hemorrhage (ICH) is a severe subtype of stroke characterized by extensive secondary brain injury driven by oxidative stress, inflammation, and progressive neuronal loss, leading to poor neurological outcomes. Thymoquinone, a bioactive compound derived from Nigella sativa, has demonstrated potent antioxidant and neuroprotective properties, but its integrated effects in hemorrhagic stroke remain insufficiently explored. This study aimed to evaluate the antioxidant and neuroregenerative effects of thymoquinone in a rat model of ICH. Methods: Male Wistar rats with experimentally induced ICH were randomized into untreated controls and two treatment groups receiving thymoquinone (150 mg/kg and 250 mg/kg) for three consecutive days. Oxidative injury and antioxidant responses were assessed using membrane blebbing, malondialdehyde (MDA), superoxide dismutase (SOD) activity, and nuclear factor erythroid 2–related factor 2 (Nrf2) expression, while neuroprotection was evaluated by neuronal counts in perihematomal tissue. Results: Thymoquinone treatment significantly reduced membrane blebbing and MDA levels, while markedly increasing SOD activity and Nrf2 expression in a dose-dependent manner. These biochemical improvements were accompanied by significant preservation of neuronal morphology and increased neuronal survival, with the 250 mg/kg dose showing the strongest effects. Conclusions: In conclusion, thymoquinone confers robust antioxidant and neuroprotective benefits in experimental ICH and represents a promising candidate for mitigating secondary brain injury following intracerebral hemorrhage.

Abstract: An elementary and self-contained proof of the existence of the Euler-Mascheroni constant γ is presented, based solely on the Simpson quadrature formula and the convexity of the function f\( x \mapsto 1/x \). The local logarithmic increments are approximated as follows: \( \int_{2n-1}^{2n+1} \frac{dx}{x} \) Using Simpson’s rule, a discrete approximation expressed as a finite linear combination of reciprocal integers is constructed. Exploiting the monotonic and convex nature of the function \( 1/x \), sharp two-sided inequalities relating the numerical approximation to exact logarithmic increments are established. These inequalities imply that the accumulated quadrature errors form a convergent series. Consequently, the following classical limits \( \gamma = \lim_{N \to \inf} \left( \sum_{k=1}^{N} \frac{1}{k} - \log{[N]} \right) \) are proven to exist. This approach provides a conceptually simple alternative to traditional proofs based on the Euler-Maclaurin formula, highlighting the direct connection between numerical integration, convexity, and the analytical nature of γ. I further show that λ can be expressed as \( (\log{[2]}+1)/3 + \delta \), where both \( (\log{[2]}+1)/3 \) and \( \delta \) are irrational, and where \( \delta \) arises as the limit of a rational sequence derived from as Simpson-type approximation.

Abstract: The question of how consciousness arises from physical systems remains one of the most profound challenges in neuroscience and philosophy. This essay examines two leading models that attempt to explain the emergence of consciousness from both biological and synthetic neural networks: Integrated Information Theory (IIT) and Global Workspace Theory (GWT). Each offers a distinct approach—one grounded in intrinsic informational structure, the other in functional accessibility and cognitive architecture. By comparing their principles, empirical support, and criticisms, this essay aims to clarify how these models contribute to our understanding of consciousness and its potential replication in artificial systems. Recent adversarial testing reveals that both theories face substantial empirical challenges, suggesting the field may need to resolve fundamental conceptual questions before definitive adjudication between theories becomes possible.

Abstract: This study as part of a postdoctoral research takes a critical look into Corporate business incubators (CBIs) value co-creation by adopting an integrative and meta-model approach. An integrative review aids the aggregation of studies fragmented with diverse views and perspective or approaches without a cogent agreeable framework; while meta-modelling aids the development of new models from an existing one. Based on paradigms with meta models from different perspectives and areas of studies (philosophical, entrepreneurial, psychological, innovation), this study aggregates them into a compounded framework of study for easier audience digest. Based on this, the study uses a multi-level analyses for the methodological synthesis of CBI value co-creation concepts capturing the holistic view based on CBIs classifications taxonomy and typology, scope and pathways based on CBI activities with an outlook on CBI Business Models. In addition to this, an emerging and captivating concept of cognitive schema of CBI actors is applied to the model capturing the attributes, relationships and CBI sub-schemas and their value co-creation outcomes based on response to environmental conditions and innate organizational capabilities. The study identifies three meta models which are based on CBIs core components and functions, CBIs Value Co￾creation and UBI (University Business Incubators), CBIs Core Business Models, Classifications and Value Co-creation and also addresses the complementarities towards a compounded CBI framework. The evolving model(s) would serve as the conceptual framework on which further CBIs co-creation research study with UBIs would be built.

Abstract: Background: Subacute sclerosing panencephalitis (SSPE) is a chronic, progressive disease of the central nervous system (CNS) caused by persistent infection at this level with the wild measles virus. Its incidence is correlated with measles vaccination coverage. The pathogenesis isn't fully understood, but infection before the age of 2 is an important risk factor. Methods: This is a retrospective observational study conducted at the Louis Turcanu Children's Hospital in Timisoara, Romania, based on the analysis of the medical records of patients diagnosed with SSPE between January 2021 and December 2025. We analyzed demographic and epidemiological factors, clinical and paraclinical findings, management, and outcomes. Results: Seven children were diagnosed during the study period, with a mean age of 8.4 years (range 7-11 years). Six of them had contracted measles during their first year of life, and one at the age of four. The mean latency period was 7.1 years (range 4-9 years). On admission, all patients presented symptoms consistent with clinical stage II, with periodic slow wave discharges on electroencephalogram (EEG). The initial brain Magnetic Resonance Imaging (MRI) was normal in two cases, while revealing varied abnormalities in all others. Despite complex treatment with isoprinosine and anticonvulsants, progressive cognitive and neurological deterioration continued in all patients. Conclusions: SSPE is a rare but serious, debilitating disease despite its complex, multidisciplinary care. Following a 10-year SSPE-free period, the reappearance of these pediatric cases constitutes a public health alert, unequivocally demonstrating the importance of measles vaccination.

Abstract: Background: Sleep disorders like insomnia, obstructive sleep apnea (OSA), REM behavior disorder etc. are nowadays diagnosed through the Internet of Things (IoT)-enabled sys-tems that monitor and analyze the subject's sleep data. Health IoT networks are rife with communications of sensitive physiological data from wearable EEG, ECG, SpO₂ and res-piratory sensors. However, these networks face threats from anomalous traffic flows, sig-nal sabotage and data integrity violation. In this paper, an AI-based hybrid detection and classification framework is proposed for secure Sleep Health IoT (S-HIoT) networks. The integrated CNN, BiLSTM and RF model provides a proposed framework for joint sleep-stage classification and network anomaly detection. To this end, a multi-objective loss function is proposed for jointly optimizing the physiological state prediction and se-cure traffic monitoring. Experimental validation using the Sleep-EDF and CICIoMT2024 datasets demonstrate a classification accuracy of 97.8% for sleep staging, and 98.6% for network detection with low inference latency (

Abstract: Short tandem repeat (STR) expansions are a major cause of neurodegenerative disorders; however, their genetic and clinical heterogeneity complicates diagnosis. STR detection remains limited in routine short-read next-generation sequencing (NGS) workflows. We evaluated the diagnostic yield and clinical utility of computational STR genotyping in a large Turkish neurodegenerative disease cohort. ExpansionHunter was applied to NGS data from 3,150 patients and 146 controls, targeting 15 disease-associated STR loci. To improve genotyping of poorly captured exonic regions in exome data, the default locus coverage threshold was reduced from 10X to 3X. Candidate expansions were visually inspected using REViewer and validated by conventional molecular methods. Computational analysis identified 28 pathogenic and 160 intermediate expansions. Of these, 23 were confirmed as pathogenic, and eight initially classified as intermediate were reclassified as pathogenic after conventional validation, resulting in 31 pathogenic cases across 28 families: HTT (n=8), ATXN2 (n=5), ATXN1 (n=4), DMPK (n=3), PABPN1 (n=3), TBP (n=2), and single cases in AR, ATN1, and CACNA1A. Lowering the coverage threshold markedly increased genotyping rates at low-coverage loci in exome, particularly in ATXN2. Genetic findings were largely consistent with clinical pre-diagnosis and the additional diagnostic yield was 0.95%. These findings support integrating STR analysis into routine neurogenetic diagnostics.

Abstract: Illyrian helmets represent a key element of Iron Age martial culture in the western Bal-kans, reflecting technological knowledge, workshop traditions, and long-distance cultural exchange. Based on the currently available archaeological record, Illyrian helmets are first attested in contexts dating to the 8th-7th centuries BC, with finds concentrated in Greece and the central and western Balkans, including Macedonia, Albania, Dalmatia, and the wider interior. Over time, the form developed into several variants (Types I-IIIB). This study presents the elemental characterization of the total set of 27 Illyrian helmets exca-vated in Albania and currently preserved in local museum collections, a region where the later types are particularly well attested. As the helmets are intact and exhibited in mu-seums, non-destructive micro-XRF analysis was employed. The main research questions addressed how the alloy composition, including minor and trace elements, reflects local metallurgical practices and distinguishes Illyrian helmets from similar helmets in neigh-boring regions. The results indicate the consistent use of bronze alloys dominated by cop-per (89-95.3%) with low tin contents (3.5-9.9%), consistent with established alloying prac-tices for durable protective equipment. Minor and trace elements, including iron (up to 1.5%), lead (up to 0.76%), arsenic (up to 0.09%), zinc (up to 1.17%), and antimony (up to 2.36%), likely reflect metallurgical choices, recycling practices, or impurities linked to re-gional copper deposits. These elemental signatures, particularly the association of arsenic, antimony, zinc, and iron, suggest regional metallurgical characteristics and offer addi-tional insight into Illyrian bronze production, while helping to distinguish these helmets from contemporaneous finds in other parts of the Balkans and Europe.

Abstract: Fibromyalgia (FM) syndrome is typified by constant and pervasive musculoskeletal pain and may be comorbid with obesity. Glucagon Peptide 1 Receptor Agonists (GLP-1RAs) are relatively new pharmacotherapies developed for the treatment of type 2 diabetes mellitus (T2DM) and repurposed for the treatment of obesity. In addition to their well-established impact on glucose balance, new evidence indicates that GLP-1RA may have anti-inflammatory properties beyond glycaemic regulation. Modulation of central pain pathways by GLP-1RAs has been proposed in patients with FM, but few studies have directly evaluated the effects of GLP-1RAs on central pain. Hence, the purpose of this study is to review the relationship between FM and obesity and explore the potential role of GLP-1RAs in the management of FM.A literature search was conducted across four da-tabases - PubMed/Medline, Cochrane, Google Scholar, and PEDro, up to May 2025. The literature was sparse, and no formal evaluation process was performed; however, papers were excluded if they failed to address either FM or GLP-1RA. There was no formal risk-of-bias assessment for each included paper. Key characteristics of each study were extracted and summarized in table form to enable efficient narrative synthesis. Of the 56 included studies, 24 were preclinical reviews, 16 were clinical reviews, 8 were preclinical animal models, and only 8 focused on human data, limited to retrospective analyses of data and self-report. There is some evidence that GLP-1RAs may reduce neuronal excita-bility, inhibit pain signalling, and decrease inflammation. However, given the lack of clinical trials, it is difficult to draw firm conclusions regarding the potential role of GLP-1RA in the management of FM with comorbid obesity.

Abstract: The heel-strike (HS) paradigm of human gait originates from 19th-century chronophotographic studies conducted on Georges Demeny,a gymnasium instructor whose performed, exaggerated gait was never representative of natural locomotion. A compounding martial bias further normalised HS through military marching drill. A multi-disciplinary convergent argument analysis is conducted, integrating philological, zoological, anatomical, biomechanical, neurological and socioeconomic lines of evidence. All seven lines of argument support a forefoot-first model in which the centre of mass (CoM) leads the movement, stabilisers control equilibrium proactively, and the Triceps surae works in continuous eccentric mode—its natural functional state. Heel-strike generates impact forces up to 700 N with an ascending braking vector, under-recruits Gluteus maximus, progressively impoverishes plantar mechanoreceptors, and transmits repeated microtraumatic impulses up to the brain. Natural human gait is organised around forefoot contact, progressive CoM advance, and continuous eccentric stabiliser activity. The proposed model rediscovers lightness: a dance with gravity rather than a war against it. The HS paradigm is a culturally conditioned artefact with measurable pathological consequences for joints, the lumbar spine, and beyond.

Abstract: The digitalization of healthcare systems increases their exposure to security incidents. Security analysts use standard CVE (Common Vulnerabilities and Exposures) records to identify and mitigate vulnerabilities. However, CVEs are often incomplete or overly generic, requiring the addition of structured, actionable information to support effective decision-making. Manually performing this augmentation is unfeasible due to the rapidly growing number of published CVEs. In this paper we evaluate the capabilities of LLMs (Large Language Models) to classify and analyze CVEs within the medical IT systems domain. We propose a framework where LLMs parse structured JSON context and answer a set of specific natural language questions, enabling the categorization of vulnerabilities by their position in the medical chain, affected component types, and mapping to the MITRE ATT&CK framework. While recent studies show that general LLMs can achieve high accuracy in objective CVSS elements and learn CNA-oriented patterns, they often struggle with subjective impact metrics. Our results demonstrate that domain-specific classification through natural language prompting provides the necessary granularity for medical risk prioritization. We conclude that this augmentation effectively bridges the gap in standard CVE records, allowing for a better understanding of how vulnerabilities impact critical healthcare infrastructure and patient safety.

Abstract: Background: Delusional Infestation is a well-documented, psychodermatological condition where patients falsely believe themselves to be parasitized. It responds well to psychiatric treatment, but the delusion causes patients to seek dermatologists or entomologists for help. Publications denying the psychological component of the illness, often without evidence, harm public health by negatively affecting patient treatment. This paper addresses a novel form of such denial called “neurocutaneous syndrome," whose proponents reject both the psychological and parasitological etiology, and instead attribute the symptoms to common dental sealants. Methods: A critical, scoping review of all relevant literature without other exclusion criteria was completed in 2026 following PRISMA guidelines to determine where this concept originated and how far it has spread. Conclusions: The results show that "neurocutaneous syndrome" as a denial of delusional infestations entered the scientific literature primarily via predatory, non-peer-reviewed, and clone journals, but also peer-reviewed dentistry journals. Valid evidence for it is nonexistent. While not accepted by the medical community, uncritical acceptance of neurocutaneous syndrome features prominently in alternative health publications. The academic literature has been slow to counter such misinformation, especially for conditions like delusional infestations that straddle the disparate fields of dermatology, psychiatry, and entomology.

Abstract: Acute myocarditis has traditionally been regarded as an acquired inflammatory disorder of the myocardium, most commonly triggered by viral infection or immune-mediated injury. However, growing evidence suggests that in a subset of patients, myocarditis may represent the initial clinical manifestation of an underlying genetic cardiomyopathy rather than a purely inflammatory disease. Recent advances in molecular genetics, cardiac magnetic resonance imaging, and translational pathology have revealed a significant overlap between myocarditis and inherited cardiomyopathies, particularly those related to desmosomal dysfunction. Desmosomal gene variants—most frequently involving desmoplakin, plakophilin-2, and desmoglein-2—have been increasingly identified in patients presenting with myocarditis-like syndromes characterized by chest pain, troponin elevation, and imaging findings fulfilling Lake Louise criteria. Importantly, these presentations often demonstrate recurrent inflammatory episodes, ventricular arrhythmias, and progressive myocardial fibrosis that ultimately evolve into the structural phenotype of arrhythmogenic cardiomyopathy. Emerging mechanistic data further suggest that structural instability of the intercalated disc may trigger autoimmune responses through exposure of desmosomal antigens and generation of disease-specific autoantibodies such as anti-DSG2. This review explores the evolving concept of myocarditis as an inflammatory manifestation of genetically mediated cardiomyopathy and highlights the diagnostic and mechanistic implications of this gene–immune interaction.

Abstract: Differential Liberation Expansion (DLE) and viscosity tests are core elements of the Pressure–Volume–Temperature (PVT) laboratory suite used to characterize reservoir oils under depletion and to support compositional modeling and reservoir simulation. Nevertheless, both DLE and viscosity testing remain expensive and time-consuming due to specialized equipment, strict operating procedures, and the need for experienced laboratory personnel.Building on our prior work that introduced the proximity-informed Local Interpolation Model (LIM) framework for Constant Composition Expansion (CCE), this study demonstrates how the same end-to-end, neighborhood-based workflow is applied to DLE and viscosity test data. A target fluid is embedded in a compositional–thermodynamic descriptor space and paired with a small set of thermodynamically similar fluids drawn from a PVT data archive. Within this locality, LIM is used to infer DLE behavior by combining local interpolation for key scalar quantities (e.g., saturation-point and endpoint PVT values) with shape-preserving reconstruction of pressure-dependent curves. For viscosity, the same approach reconstructs the oil-viscosity curve across the undersaturated and saturated regions. Evaluation on a proprietary database of DLE and viscosity tests shows strong agreement across diverse fluids for both DLE and oil viscosity trends. This supports reducing reliance on new DLE and viscosity measurements while maintaining engineering-grade fidelity in reservoir-engineering and simulation workflows. This approach has been fully automated through software so it can be set up and directly utilized by the field operators on their own databases to significantly reduce their fluid sampling and laboratory analysis costs. Moreover, the proposed AI model does not use others’ data while respecting data privacy and data ownership.