Abstract: Warfarin is a coumarin-derived oral anticoagulant widely used for the prevention and treatment of thromboembolic disorders, particularly in patients with mechanical heart valves. The drug exerts its anticoagulant effect by inhibiting vitamin K epoxide reductase, thereby impairing γ-carboxylation of vitamin K–dependent coagulation factors. Despite its clinical efficacy, warfarin therapy is associated with a narrow therapeutic index, substantial interindividual variability in dose response, numerous drug interactions, and significant hemorrhagic risk. Maternal warfarin therapy during pregnancy is strongly associated with fetal warfarin syndrome (FWS), a characteristic pattern of embryopathy resulting from in utero exposure to the drug. This review summarizes current knowledge regarding the physicochemical properties, pharmacological mechanisms, dose variability, toxicity, and developmental effects associated with warfarin exposure. Evidence from human clinical studies and vertebrate animal models is discussed to elucidate conserved developmental and molecular mechanisms underlying warfarin teratogenicity. The review also examines signaling pathways disrupted by warfarin exposure that are involved in bone morphogenesis, vascular homeostasis, and tissue mineralization, contributing to the observed phenotypes. Collectively, this review integrates clinical, molecular, and experimental findings to provide a comprehensive understanding of warfarin-induced developmental toxicity. Current knowledge is insufficient to fully elucidate the complex mechanisms underlying warfarin-induced embryopathy and fetal toxicity. Further investigations are warranted to identify safer anticoagulant regimens during pregnancy and to inform the development of novel therapeutic strategies that minimize fetal risk while maintaining maternal anticoagulation.

Abstract: Epidemics spread through contact, movement, behavior, and public health interventions, an inherently relational dynamic in which the infection travels along connections between people, places, or animal species. For this reason, we need mathematical and computational models capable of explicitly representing these connections. This paper introduces the theoretical foundations of network-based epidemic models, such as SIR and SEIR, and demonstrates how graph neural networks (GNNs) can learn the spatiotemporal patterns of transmission from data, overcoming the limitations of classical models. Three case studies are presented: measles, i.e., uneven vaccination coverage, COVID-19, i.e., targeted vaccination of the most central nodes in the contact network, and hantavirus, i.e., a multilevel model linking rodents, the environment, the molecular response, and human-to-human transmission). Since public health decisions must be justifiable, the work devotes particular attention to the explainability of the models: identifying which individuals, contacts, or territories are most critical and which alternative interventions could change the outcome of an epidemic. Finally, an operational pipeline is outlined to translate complex data into reliable and transparent decision support.

Abstract: With urbanization resulting in increased demand for indoor comfort, HVAC (heating, ventilation, and air-conditioning) systems, particularly air handling units (AHUs), are essentials for indoor climate control. The advent of big data and artificial intelligence (AI) have opened new avenues for enhanced safety and reliability in HVAC operations. Hence, this study focused on the predictive performance evaluation of AHUs, which is receiving less attention compared to its fault detection and optimal control issues. Utilizing real-time operational data from Oak National Laboratory, the proposed model employs multi-task learning (MTL) to refine prediction accuracy for AHU return air properties, including temperature, moisture content, and power consumption. This is achieved without allowing any single task to dominate others during the training phase. Moreover, the model introduces an ensemble approach that synergizes the capabilities of the different MTL algorithms using a boosting technique via gradient boosting regression tree (GBRT). This novel strategy has demonstrated superiority over conventional data-driven approaches in terms of performance. The paper culminates by showcasing the significant role of the proposed model as a metric for AHU performance evaluation and its contribution to smart decision-making in a real-world context. In essence, the developed model is poised to facilitate optimal decision-making regarding HVAC components and foster proactive strategies to ensure consistent operation and extend the lifespan of HVAC systems.

Abstract: Community extension programs in higher education often remain limited to service or livelihood training, leaving their pedagogical potential underexplored. This study, conducted in Academic Year 2024–2025 at St. Michael’s College of Iligan, Inc., investigates U-Rock, a restorative community extension initiative designed in partnership with Bahay Pag-asa to reframe extension program as education. Anchored in Critical Pedagogy, Restorative Education, Psychosocial Wellness Theories, and Interdisciplinary Pedagogy, the program integrates literacy, catechism, and wellness activities into structured learning experiences for the youth in conflict with the Law. Addressing existing gaps, the study engages juvenile offenders within HEI extension program, develops an interdisciplinary model that combines literacy, catechism, and wellness support, and reframes extension from service into pedagogy. Using a multi-method qualitative approach that integrates case study, narrative inquiry, and ethnographic observation, the research involved thirty participants including students, faculty, Bahay Pag-asa personnel, parents, and youth residents. Findings reveal that U-Rock empowers youth through literacy, moral reflection, and psychosocial resilience while simultaneously transforming SMCII students through empathy, civic responsibility, and applied learning. Ten salient themes emerged, highlighting Extension as Pedagogy, Operationalizing CHED Mandates, Literacy as Empowerment, Values Formation and Moral Reflection, Psychosocial Resilience, Interdisciplinary Collaboration, Student Transformation, Institutional Partnership and Mission Alignment, Distinction from Conventional Models, and Contribution to Global Curriculum Discourse. Collectively, these insights demonstrate that community extension program can function as restorative pedagogy, advancing community rehabilitation and institutional mission, while offering a replicable model for higher education institutions to innovate curriculum and respond to the needs of significant youth in the periphery.

Abstract: The growing deployment of large-scale models in the power industry improves grid operation and decision-making. However, it also introduces security concerns, such as adversarial reasoning attacks and data manipulation. To address these challenges, this paper proposes a Trusted Execution Environment (TEE)-based secure reasoning framework enhanced with Explainable Artificial Intelligence (XAI). XAI methods are integrated to identify critical input features and detect anomalies by analyzing abnormal feature attribution patterns. The framework ensures that sensitive data and reasoning processes are securely executed within hardware-isolated environments while maintaining interpretability and operational transparency. Experimental results in simulated power grid scenarios demonstrate that the proposed approach significantly improves both the security and explainability of large model reasoning, offering a reliable defense mechanism for critical power infrastructures.

Abstract: Grapevine trunk diseases (GTDs) are major constraints to vineyard longevity and productivity worldwide, and pruning wounds are recognized as key infection courts for their causal fungi. However, the dynamics of natural infection after pruning under field conditions remain insufficiently defined. This study evaluated natural infection of grapevine pruning wounds by GTD pathogens in three commercial vineyards in Spain and France over two growing seasons. At each site, vines were pruned in the dormant season either early (November-December) or late (February), and wounds were sampled weekly for 8 weeks. Recovery-based disease severity was quantified using the percentage of wood pieces yielding GTD pathogens after isolation. A total of 11,230 fungal isolates were recovered, of which Botryosphaeriaceae accounted for 54.4%, followed by Diaporthe spp. (34.2%) and Cytospora spp. (11.4%). The dominant species identified was D. seriata. Recovery-based disease severity varied significantly over time in all site-disease combinations, and temporal trajectories differed with pruning time and season. Late pruning resulted in significantly greater recovery-based disease severity than early pruning in 6 of 9 site-disease combinations. The strongest effect was observed in Pyrénées-Atlantiques for Botryosphaeria dieback, where late pruning increased severity by 18.8%; Cytospora canker at the same site increased by 7.2%. Climatic analyses revealed site-specific associations, with relative humidity showing the strongest association with recovery-based disease severity in Pyrénées-Atlantiques and rainfall in Pyrénées-Orientales. These results indicate that GTD pathogens can be recovered from pruning wounds for at least 8 weeks after pruning and that the effect of pruning time is strongly site- and pathogen-dependent.

Abstract: LeJEPA learns representations by combining predictive alignment with isotropic Gaussian regularization, but the rotational symmetry of the Gaussian target leaves learned coordinates identifiable only up to orthogonal transformations. We introduce LeJEPA-FGP, a structured extension that replaces the pointwise isotropic Gaussian embedding law with a factorized Gaussian-process law over indexed representations. Each latent channel follows its own GP covariance kernel across an index such as time, space, or pose, while remaining marginally standard Gaussian at each point. We propose FGP-SIGReg, a sketched characteristic-function regularizer for matching factorized GP embedding laws. We prove that FGP-SIGReg consistently matches finite-dimensional factorized GP distributions. Under GP-OU positive-pair transitions, alignment with exact FGP law matching forces the representation into the first Wiener chaos, yielding linear recovery of the latent GP path. Under shared index-local encoders, distinct covariance kernels reduce the residual ambiguity from orthogonal rotations to signed permutations, while repeated kernels yield block-orthogonal ambiguity. Synthetic GP experiments validate signed-permutation recovery under distinct kernels and block recovery under kernel collision.

Abstract: Background: Renal cell carcinomas (RCC) represent neoplasms with variable biological behaviour. Some remain difficult to classify within the current diagnostic framework. Clear cell papillary renal cell tumour (CCPRCT) is now recognized as an indolent entity, whereas renal cell carcinoma with fibromyomatous stroma (RCCFMS) remains a provisional subtype with partially overlapping morphological features. Methods: We analysed a multifocal RCC with clear cell morphology and prominent fibromyomatous stroma using whole-genome and RNA sequencing. The molecular profile was compared with the Cancer Genome Atlas (TCGA) pan-cancer dataset including 885 RCC cases. Histological re-evaluation of 10 identified similar cases was performed. Transcriptional data was mined for potential markers which were validated in an independent cohort. Results: The ten TCGA cases with similar transcriptomic features were characterized by diploid genomes, absence of recurrent chromosomal alterations and lack of VHL gene mutations. Reduced VHL mRNA expression was observed, with increased methylation at selected CpG sites consistent with possible epigenetic down-regulation. Histological re-evaluation by three urological pathologists identified diagnostic variability. Differential expression analysis highlighted cytokeratin 17 (KRT17) and collagen 17A1 (COL17A1) as candidate markers. Immunohistochemical evaluation in a small (n = 6) independent cohort of CCPRCT demonstrated expression of both markers, whereas tissue microarrays of 257 clear cell and 68 papillary RCC cases were found to be negative. Conclusions: These findings suggest that a subset of renal tumours with overlapping morphological features of CCPRCT and RCCFMS may share common molecular characteristics. These observations are exploratory and hypothesis-generating, and further studies in larger, well characterized cohorts are required to clarify the biological and diagnostic significance of this subgroup.

Abstract: International communication scholarship has undergone a paradigmatic reorientation since 2000, yet the field’s conceptual repertoire has expanded more rapidly than it has been theoretically integrated. This systematic literature review interrogates that fragmentation by mapping the trajectory of the field across the period 2000–2026 and assessing the extent to which its proliferating frameworks—cultural imperialism, hybridization, network society, platform imperialism, data colonialism, computational propaganda, sharp power, and algorithmic governance—constitute cumulative theoretical advancement or analytically incommensurable parallel vocabularies. Following PRISMA 2020 procedures (Page et al., 2021) and a thematic synthesis design (Thomas & Harden, 2008), the review consolidates peer-reviewed scholarship across seven major communication databases into seven thematic clusters: cultural globalization and media flows; comparative journalism and cross-national media systems; de-Westernization and decolonial currents; platformization, digital sovereignty, and media infrastructures; disinformation, computational propaganda, and information disorder; soft power, public diplomacy, and affective strategic communication; and the integration of generative artificial intelligence into transnational communication. Three theoretical findings emerge. First, the apparent succession of paradigms from broadcast-era to platform-era frameworks is better understood as conceptual layering, in which power-asymmetric models persist in modified form rather than being displaced by network-based alternatives. Second, the field’s longstanding tension between structural and agentic accounts has been reconfigured—but not resolved—by the platform turn, with infrastructural analysis emerging as a potential synthesizing register (Parks & Starosielski, 2015; Plantin & Punathambekar, 2019). Third, the persistent disjuncture between the field’s de-Westernization commitments and its bibliometric realities (Demeter, 2020) is theoretically consequential, indicating that epistemic asymmetries function not as residual artifacts but as constitutive features of contemporary international communication knowledge production. A seventh identified gap—the under-theorization of affective dimensions of international communication—extends the review’s analytic horizon to include emergent comparative work on emotion, civilizational rhetoric, and cross-border public engagement (Çelik, 2025; Hameleers & Garnier Ortiz, 2024; Wahl-Jorgensen, 2019). The review proposes a future research agenda centered on epistemic pluralism, methodological diversification, infrastructural and material analysis, and sustained engagement with planetary-scale technological change.

Abstract: Commercial manufacturing of prebiotics relies on diverse processing steps tailored to the raw material and finished product. Commercial manufacturing operations are distinct from processes suggested in the research literature, accounting for scalability, cost and environmental metrics, and process reproducibility. Common prebiotic production processes involve extraction, hydrolysis using enzymes, acids or hot water, synthesis, condensation polymerization and precision fermentation. Crude extracts are purified using ion exchange, activated carbon, and membrane separation processes to remove impurities and produce prebiotic oligosaccharides with the targeted composition and degree of polymerization. Purified extracts are often concentrated using evaporation systems and may be dried or crystallized to produce a dry finished product. Though not an all encompassing list of prebiotics, detailed descriptions of processes for the production of some of the more common prebiotics, including acacia, fructo-oligosaccharides (FOS), galacto-oligosaccharides (GOS), human milk oligosaccharides (HMOs), inulin, mannan-oligosaccharides (MOS), certain types of resistant starch (RS), xylo-oligosaccharides (XOS), and arabinoxylan oligosaccharides (AXOS) are provided in this manuscript, along with descriptions of commercial scale unit operations that may be applied more generally to newer compounds being investigated for their prebiotic properties. The unique attributes of each type of prebiotic and prebiotic formulation, particularly the degree of polymerization and chemical structure, are strongly controlled by the processes employed in their manufacture. Strategic selection of enzymes (or hydrolysis processes in general), fermentation systems, and extraction systems/solvents will influence the product composition and degree of polymerization, leading to a diverse array of products. Regulatory requirements and quality control systems employed during manufacturing of prebiotics ensure that finished products are safe and effective for consumers, delivering the expected health and physiological benefits.

Abstract: The notion of a vortex is fundamental in fluid dynamics, where it broadly refers to rotary fluid motions of various forms. Yet a precise, universal definition has remained elusive. Two fundamental challenges persist in the existing definitions of a vortex. The first is the gap between the analytic perspective that adopts a framework of motion decomposition and the synthetic perspective that emphasizes the geometric patterns of the composite motion. The second is the gap between precisely defined local measures of rotation and intuitive large-scale descriptions of vortices. This paper develops a geometric theory of smooth tangent vector fields on oriented closed surfaces that bridges the analytic and synthetic perspectives, and provides a nonlocal definition of a vortex core. Working within the frameworks of the irreducible symmetric-antisymmetric decomposition (iSAD), eigenvalue decomposition (EVD) and Helmholtz-Hodge decomposition (HHD), we prove two principal results for such vortex cores. First, all streamlines therein wind in the same direction, indicating a nonlocal rotary motion in the entire vortex core. Second, each vortex core can have at most one axis (center or focus). The theory is illustrated with examples on spheres and tori of various curvature, demonstrating how geometry and topology shape the shape of vortex cores. The results are purely mathematical and extend naturally to open surfaces, offering a rigorous foundation for vortex identification across disciplines.

Abstract: Background: Thalassemia represents the world’s most prevalent inherited hemoglobin disorder, affecting approximately 4.4 per 10,000 live births globally. Accurate genetic characterization is indispensable both for definitive diagnosis and for lifetime clinical monitoring. The past two decades have witnessed a paradigm shift from conventional protein-based assays toward comprehensive molecular techniques, including next-generation sequencing (NGS), third-generation (long-read) sequencing, and preimplantation genetic testing for monogenic disease (PGT-M). Objectives: (1) To systematically evaluate the molecular techniques available for confirming the diagnosis of alpha- and beta-thalassemia, including their diagnostic accuracy, indications, and limitations; (2) to examine how genotype–phenotype correlation and genetic modifier profiling inform clinical prognosis and therapeutic decision-making; and (3) to define evidence-based genetic monitoring parameters for longitudinal follow-up of patients receiving transfusions, iron chelation, and novel curative therapies including gene therapy. Methods: A comprehensive narrative review was conducted by systematically searching PubMed/MEDLINE for English-language peer-reviewed articles published between January 2000 and December 2024. Forty-three studies were ultimately included after applying predefined inclusion and exclusion criteria. Quality of included studies was assessed using SANRA (Scale for the Assessment of Narrative Review Articles). Results: HPLC and capillary electrophoresis remain first-line phenotyping tools; DNA-based confirmation is mandatory for complete genotyping. NGS-based targeted panels detect >95% of common mutations but require MLPA co-testing or long-read sequencing for structural variants. Genotype–phenotype prediction is substantially enhanced by profiling three major modifier loci: XmnI (Gγ), BCL11A, and HBS1L-MYB. PGT-M using NGS achieves near-complete genotyping accuracy (>99%) with live birth rates of 40–60% per frozen embryo transfer cycle. For patients receiving curative gene therapy (exagamglogene autotemcel / Casgevy), molecular follow-up protocols spanning 15 years are now recommended. Cardiac T2* MRI remains the most reliable non-invasive tool for iron overload follow-up, superior to serum ferritin alone. Conclusion: A tiered, genotype-informed approach—combining HPLC/CE phenotyping, targeted molecular diagnostics, genetic modifier profiling, and periodic re-evaluation—optimizes diagnostic precision and guides individualized management across the thalassemia spectrum. Integration of PGT-M and long-read sequencing into standard care pathways, alongside robust gene therapy follow-up protocols, will define the next era of thalassemia genetics.

Abstract: We describe a harmonic analysis system for predicting annual peak snow water equivalent (SWE) at SNOTEL monitoring stations operated by the Natural Resources Conservation Service (NRCS) across the western United States. The algorithm, frqsrchX, performs greedy harmonic regression on historical SWE records, identifying persistent periodic climate signals and superimposing volcanic impulse functions to account for episodic radiative forcing from major eruptions. A five-phase characterization pipeline applies distinct band-limited search strategies per site, and a two-winner selection system identifies optimal configurations by both maximum pass rate and a reliability score that balances accuracy with period stability. Validation uses out-of-sample holdout testing across 15–18 years (2008–2025), graded by an asymmetric scale that penalizes over-prediction more harshly than under-prediction. We report results for 771 SNOTEL and SNOW SENSOR stations across eight western states. Average pass rates range from 88.4% (Montana, 94 sites) to 49.3% (California, 122 sites, including 87 SNOW SENSOR stations). The three commercially targeted states—Colorado (113 sites), Montana (94 sites), and Wyoming (87 sites)—achieve average pass rates of 86.4%, 88.4%, and 84.2% respectively, with 84–90% of sites meeting the ≥80% operational pass-rate threshold using identical universal parameter search procedures and no state-specific tuning. Idaho (85 sites) and Washington (76 sites) show strong intermediate performance at 83.3% and 81.5%. Utah and Oregon show mixed results, while California falls well below operational thresholds. Period stability analysis indicates that 55–62% of qualifying sites in the five strongest states achieve stable signal detection, demonstrating consistent identification of physical climate periodicities. These results demonstrate that periodic climate signals—principally in the ENSO band (2,700–2,900 mY), a mid-range band (~6,000–7,500 mY), and an extended long-period band (10,500–17,000 mY)—carry actionable predictive information about annual peak snowpack at individual station scale. The 2026 season, the first fully prospective test, produced a near-universal over-prediction across all commercially targeted states. A station-level decomposition for Colorado attributes this outcome predominantly to rain-versus-snow partitioning during a warm snow drought (53% of the SWE shortfall) rather than to a precipitation-forecasting failure. We accordingly treat warm-drought years as outside the method’s scope and ungraded, while precipitation-driven (dry) snow drought—a deficit the method does forecast—remains in scope. The historical validation reported here is unaffected.

Abstract: Bacterial meningitis is a life-threatening central nervous system infection in which rapid and accurate pathogen identification is essential for effective treatment; however, conven-tional culture methods often show limited sensitivity due to prior antibiotic exposure, low microbial load, or fastidious organisms. Background: This study aimed to investigate the cerebrospinal fluid (CSF) microbiota in patients with bacterial meningitis and to evaluate the diagnostic performance of 16S rRNA gene-based metagenomic next-generation se-quencing (mNGS), particularly in culture-negative cases. Methods: CSF samples from 26 patients collected between September 2023 and July 2025 were analyzed. Standard aerobic culture and PCR were performed. DNA was extracted using the ZymoBIOMICS kit and sequenced on the Oxford Nanopore MinION platform using the ONT 16S Barcoding Kit. Sequencing data were processed using Dorado basecalling, FastQC quality control, and taxonomic classification against NCBI and proprietary databases. Results: Conventional culture identified pathogens in 3/26 samples (Klebsiella pneumoniae, Enterobacter aerogenes, Enterococcus spp.), and one sample was PCR-positive for Mycobacterium tuberculosis. In contrast, mNGS detected bacterial pathogens in five samples, confirming all cul-ture-positive organisms and additionally identifying Mycobacterium spp. and Neisseria meningitidis operational taxonomic units in culture-negative cases. Conclusions: 16S rRNA-based mNGS demonstrated higher diagnostic yield than conventional culture and provided complementary value in pathogen detection, particularly in culture-negative meningitis, and may improve clinical diagnostic workflows.

Abstract: Agentic AI systems depend on classical public-key cryptography for agent identity, tool invocation, inter-agent communication, model integrity, and persistent state, exposing them to a cryptographically relevant quantum computer (CRQC) along two axes: confidentiality (harvest-now-decrypt-later) and integrity (harvest-now-forge-later). Existing post-quantum migration guidance addresses static, operator-controlled enterprise estates, while emerging agent-identity work omits post-quantum cryptography entirely; neither treats non-human-identity-dense, runtime-negotiated agentic systems as a distinct migration class. This paper develops a conceptual framework that does. It organizes agentic cryptography into seven migration surfaces and separates each identity into a credential layer (symmetric, operator-held, low-risk) and a trust-anchor layer (the asymmetric roots that underwrite the fleet). These layers scale inversely: a small set of trust anchors carries a forge-later blast radius equal to the population beneath it, so migration effort and forge-later risk rank the work in opposite orders. A migration matrix and a parametric effort-and-risk model formalize this, yielding the core sequencing rule: migrate anchors first. Because agentic adoption is ongoing, it also reframes migration from a finite inventory into a continuously regenerating problem, distinguishing remediation of the installed base from prevention of new classical-cryptographic debt in future deployments. It closes with oversight and procurement implications for federal post-quantum readiness.

Abstract: Limited access to quality obstetric ultrasound and trained providers remains a barrier to early pregnancy risk detection in low-resource settings. This qualitative phenomenological study explored trainer and trainee experiences of Zimbabwe’s six-week Basic Obstetric Ultrasound Short Course, delivered through the Fetal Medicine Units at Sally Mugabe Central Hospital and Parirenyatwa Group of Hospitals, and examined its perceived relevance to promoting healthy pregnancy. Sixteen semi-structured interviews were conducted between September and October 2023 with eight trainers and eight trainees purposively selected from training records. Audio-recorded interviews were transcribed and analysed using Moustakas’ transcendental phenomenological approach, including horizontalisation, clustering of significant statements, and theme synthesis. Four themes emerged: interactive and hands-on teaching and learning; confidence and skill development through supervised practice; interprofessional learning that reduced hierarchical barriers; and sustainability concerns related to equipment, refresher training, mentorship, and post-training practice. Participants described the course as practical, confidence-building, and relevant to earlier recognition of pregnancy complications, while also emphasising the need for continued supervision and system support. The findings suggest that structured basic obstetric ultrasound training may support healthy pregnancy promotion by strengthening frontline capacity for antenatal risk detection, triage, and referral, although clinical outcome effects require further evaluation.

Abstract: Fast synthetic aperture radar (SAR) imaging simulation is required by many computer vision applications. Although the shooting and bouncing ray (SBR) method has significantly accelerated electric field calculation, the number of ray tubes is still the bottleneck for SAR image simulation speed. This letter proposes an innovative adaptive SBR method driven by Q-learning for accelerated SAR imaging simulation. The core strategy is to convert the ray tube allocation into a reinforcement learning problem. The ray-shooting plane is dynamically partitioned into localized patches, where a Q-learning agent intelligently scales the ray density in real-time. By observing the geometric features of the target surface, the agent learns to employ coarser ray tubes in flat regions to eliminate redundant computation, while deploying denser ray tubes in complex areas. A multi-objective reward function is designed to balance accuracy against computational resource consumption. Numerical experiments demonstrate that the proposed Q-learning-based SBR method drastically reduces computational cost while preserving imaging similarity.

Abstract:

Toxoplasma gondii is a zoonotic protozoan transmitted by environmentally persistent oocysts and by tissue cysts in infected prey or meat. This structured narrative review compares infection evidence in five wild cervid species and three wild canid species to examine how feeding ecology shapes exposure and to assess their complementary value in wildlife surveillance. Peer-reviewed literature published between 2004 and 2025 was retrieved from PubMed, Scopus, ScienceDirect, and Google Scholar. Studies reporting evidence of T. gondii exposure or infection in wild cervids or wild canids were included, with serological evidence evaluated separately from molecular or histological detection. Cervids showed geographically variable exposure consistent with ingestion of oocysts from contaminated vegetation, soil, and water, supporting their use as sentinels of environmental contamination. Wild canids often showed higher reported seropositivity, although direct comparisons were limited by assay, sampling, and demographic heterogeneity. Their predatory, scavenging, and omnivorous diets allow access to both environmental oocysts and tissue cysts. Cervids and canids should therefore be treated as complementary rather than interchangeable indicators: cervids primarily reflect environmental exposure, whereas canids integrate environmental and trophic transmission. Standardized diagnostics, paired host–environment sampling, and explicit ecological metadata are needed to strengthen One Health surveillance and food-safety assessment.

Abstract: The use of industrial control systems (ICS) in the nuclear sector is widespread and is already considered one of the critical assets to evaluate in defining possible attack surfaces for malicious actors linked to crime or hostile nations. The risk of insider threats, both internal and related to equipment suppliers and the supply chain, is always high and must be monitored in dedicated manner with specific protocols, penetration tests and stress analysis. The typical approach to cybersecurity in IT (information technology) systems is not suitable for managing Operational Technology (OT) systems security. The advent of artificial intelligence (AI) is certainly an additional tool for enhancing defense, but it can also be a tool for possible attacks and a risk for the nuclear system in its intimate control of critical process. In this paper, we intend to review and compare various relevant standards that offer deep and comparable rules on ICS and general issues pertaining to the use of AI in most systems contextualized in nuclear domains, what institutions and international organizations are doing to exploit its advantages, and highlight the possible risks, referred to standards and guidelines.

Abstract:

Let \(X = \left( X_{t} \right)_{0 \leq t \leq T}\) be a real-valued continuous process. For a threshold \(a\), the sub-threshold time set \[E_{T}(a) = \{ t \in \lbrack 0,T\rbrack:X_{t} \leq a\}\] encodes several different threshold observables. The most elementary one is the cumulative occupation time \[A_{T}(a) = \int_{0}^{T}\mathbf{1}_{\{ X_{t} \leq a\}}\, dt.\] For a regular one-dimensional diffusion, the classical occupation density formula gives \[A_{T}(a) = \int_{- \infty}^{a}\frac{L_{T}^{y}(X)}{\sigma^{2}(y)}\, dy,\] and hence \[\frac{\partial A_{T}}{\partial a}(a) = \frac{L_{T}^{a}(X)}{\sigma^{2}(a)}.\] Thus additive threshold occupation admits a local-time sensitivity calculus. In the terminology of barrier contracts, this additive clock is the cumulative, non-resetting Parisian clock, also called the Parasian clock. The purpose of this paper is to contrast this additive/Parasian regime with the behavior of resetting Parisian burst functionals. The connected components of \(E_{T}(a)\) represent sub-threshold episodes. We study in particular the longest burst \[M_{T}(a) = \sup\{|I|:I\text{ is a connected component of }E_{T}(a)\}.\] While \(A_{T}\) is locally controlled by local time, \(M_{T}\) is governed by the connectivity of the sub-threshold time set. We prove that \(M_{T}\) is monotone, that its supremum is attained, and that the weak-sublevel version is right-continuous with left limits, while the strict-sublevel version is its left-continuous regularization. The jump at a level is the increase in the maximal connected-component length produced by adjoining the level set. This gives a deterministic càdlàg/càglàd calculus for longest-burst profiles. For regular one-dimensional diffusions, this yields a sharp structural contrast. At deterministic levels which are almost surely not local-extreme values, the weak and strict longest bursts agree almost surely. Whenever the path has a unique interior maximum, the level-indexed longest-burst profile has a positive jump at the maximum level and is therefore not absolutely continuous. Brownian motion satisfies this criterion almost surely. We further identify the deterministic mechanism behind this instability: small threshold increases may fill short temporal bridges and merge large sub-threshold components. Finally, we show that the longest burst is exactly a one-sided continuous Parisian functional. This yields an exact Laplace-transform representation of its Brownian law through the Chesney--Jeanblanc-Picqué--Yor [1] Parisian transform, and an excursion-measure formulation in which local time enters only as the Itô excursion intensity. We also discuss smoothed burst statistics, moving thresholds, and diffusion examples. The paper is intended as a threshold-sensitivity comparison: local time controls cumulative Parasian occupation, whereas resetting Parisian burst observables are controlled by component mergers and excursion structure.