Abstract: The convergence of Neuro-Symbolic AI, Edge Computing, and Reinforcement Learning heralds a transformative era in autonomous engineering design, addressing longstanding challenges in optimization efficiency, real-time responsiveness, and interpretability. Traditional design workflows suffer from siloed neural pattern recognition lacking logical rigor, centralized cloud dependencies creating latency bottlenecks, and heuristic optimization struggling with multi-objective trade-offs in vast design spaces. This paper introduces an integrated framework that synergistically combines these paradigms to create self-sustaining, end-to-end autonomous pipelines for complex engineering applications from aerospace structures to precision manufacturing.Neuro-Symbolic AI fuses deep neural networks for perceptual feature extraction with symbolic reasoning engines enforcing hard constraints and generating auditable proofs, enabling systems that both discover novel configurations and validate them against domain physics. Edge Computing decentralizes inference across device-fog-cloud hierarchies, achieving sub-10ms decision cycles critical for real-time applications like robotic assembly or smart grid stability. Reinforcement Learning optimization engines navigate continuous state-action spaces representing design variables, iteratively refining solutions through shaped rewards aligned with Pareto-optimal engineering objectives such as minimizing mass while maximizing strength-to-weight ratios.The proposed architecture orchestrates these components via directed acyclic graphs of containerized microservices, with federated synchronization ensuring data consistency across distributed nodes and human-in-the-loop interfaces providing strategic oversight for safety-critical decisions. Mathematical formulations ground the system hybrid loss functions balance learning objectives, edge partitioning optimizes, and multi-agent RL decomposes collaborative design tasks.Deployed on resource-constrained edge platforms, this framework demonstrates 8-12× acceleration in design cycle times, 25-35% improvements in structural efficiency, and full traceability satisfying aerospace certification standards (DO-178C). By eliminating manual iteration bottlenecks while preserving human insight where needed, the system redefines engineering practice, enabling rapid innovation across domains requiring concurrent optimization of performance, manufacturability, sustainability, and cost.

Abstract: The thermo-mechanical behavior of masonry materials is investigated through an in-tegrated experimental testing and numerical modelling approach. The study focuses on the characterization of masonry under fire exposure, where coupled thermal and mechanical effects govern material response and failure mechanisms. A multi-scale framework is proposed to link physico-chemical transformations, material-level prop-erties, and structural-scale behavior. The experimental component includes full-scale fire-resistance tests on load-bearing masonry walls, providing temperature evolution, deformation histories, and observed damage patterns. These results enable the identi-fication of key mechanisms such as stiffness degradation, cracking, and the influence of thermal gradients on structural response. The experimental observations are used to support the development and calibration of numerical models capable of representing temperature-dependent behavior and strain-rate effects. In addition, non-destructive testing techniques are incorporated to relate internal damage to measurable diagnostic signals, enhancing material characterization and structural assessment. Although the present study is limited to structural-scale validation, the proposed approach demon-strates how combined experimental and numerical strategies can be used to develop consistent constitutive descriptions of masonry materials. The results contribute to improved understanding and modelling of engineering materials subjected to coupled thermo-mechanical loading.

Abstract: Polymer composites used in structural applications are frequently exposed to combined thermal and moisture environments, which gradually degrade their mechanical performance over time. Predicting this behavior remains challenging due to the complex interaction between moisture diffusion, thermally activated degradation, and evolving mechanical response. In this study, a physics-based digital twin framework is developed to model the coupled hygro–thermo–mechanical degradation of fiber-reinforced polymer composites. The approach integrates moisture diffusion based on Fickian principles, temperature-dependent degradation described using Arrhenius kinetics, and a coupled modulus evolution model to represent time-dependent material behavior. The results capture key physical trends, including moisture saturation behavior, gradual stiffness reduction, and stable damage evolution under moderate environmental conditions. In addition, the influence of fiber volume fraction and temperature is systematically examined, highlighting their important roles in governing degradation resistance and long-term durability. Rather than relying on data-driven methods, the proposed framework is grounded in physically interpretable mechanisms, providing a transparent and computationally efficient tool for durability assessment. The model is presented as a qualitative benchmarking framework in the absence of system-specific calibration, with clear potential for future experimental validation and probabilistic extensions.

Abstract: Background: Malnutrition and systemic inflammation are increasingly recognized as important determinants of prognosis in patients with heart failure. Several immuno-nutritional indices, including the Prognostic Nutritional Index (PNI), the Controlling Nutritional Status (CONUT) score, and the C-reactive protein–albumin–lymphocyte (CALLy) index, have been proposed as markers of nutritional and inflammatory status. However, their prognostic value in elderly patients with heart failure with preserved ejection fraction (HFpEF) remains incompletely defined. This study aimed to evaluate the prognostic significance of these immunonutritional indices in elderly patients with HFpEF over a long-term follow-up period. Methods: This retrospective observational study included 200 elderly patients hospitalized with HFpEF (mean age 86.6 ± 6.5 years). Clinical, laboratory, and echocardiographic parameters were collected at admission. Nutritional status was assessed using PNI, CONUT score, and CALLy index. Patients were followed for mortality during long-term follow-up. Survival analyses were performed using Cox regression models, receiver operating characteristic (ROC) curves, and Kaplan–Meier analysis. Median follow-up was 3.8 years (IQR 2.1–5.9). Results: During follow-up, 123 patients (61.5%) died, while 77 patients (38.5%) were alive at the end of observation. In univariate analysis, PNI, CONUT score, left ventricular ejection fraction (LVEF), and the tricuspid annular plane systolic excursion to systolic pulmonary artery pressure (TAPSE/sPAP) ratio were significantly associated with mortality. In multivariate analysis, the CONUT score, LVEF, and the TAPSE/sPAP ratio remained independent predictors of mortality. ROC analysis showed strong prognostic performance for the TAPSE/sPAP ratio (AUC 0.932), CONUT score (AUC 0.925), and LVEF (AUC 0.897). Optimal cut-off values for mortality prediction were CONUT ≥6, LVEF ≥65%, and TAPSE/sPAP ≤0.55. Kaplan–Meier analysis confirmed significantly reduced survival among patients with higher CONUT scores, higher LVEF, and an impaired TAPSE/sPAP ratio. Conclusions: In elderly patients with HFpEF, nutritional status and cardiopulmonary functional parameters are important determinants of long-term prognosis. The CONUT score emerged as the most informative immunonutritional index, while echocardiographic parameters reflecting ventricular function and right ventricular–pulmonary arterial coupling provided additional prognostic information. Integrating nutritional assessment with echocardiographic evaluation may improve risk stratification in elderly patients with HFpEF.

Abstract: The growing emphasis on environmental sustainability within the European Union raises important questions about the nature and internal structure of corporate envi-ronmental effort. This study examines environmental expenditures - measured as in-termediate consumption of environmental protection services - and environmental investments - measured as gross fixed capital formation for environmental protection - in ten EU member states over the period 2015-2022, using data from the Eurostat En-vironmental Protection Expenditure Accounts. The analysis is conducted at both the national and sectoral levels and covers four NACE Rev.2 sectors: agriculture, mining, manufacturing, and electricity. The results reveal a pronounced asymmetry, with en-vironmental expenditures consistently exceeding environmental investments, sug-gesting that environmental effort is more strongly oriented towards maintenance than transformation. This asymmetry varies substantially across countries and even more across sectors: agriculture displays a strongly expenditure-dominated profile, whereas the electricity sector shows a more balanced pattern. On the basis of the relative inten-sity of expenditures and investments, the study proposes an interpretative four-quadrant typology of environmental strategies, distinguishing active transfor-mation, investment focus, maintenance mode, and passive profiles. The findings high-light the importance of sectoral disaggregation and show that the internal composition of environmental effort is as informative as its overall level.

Abstract: Background/Objectives: Diving exposure can cause auditory injury involving both middle and inner ear structures. Inner ear barotrauma (IEB) and inner ear decompression sickness (IEDCS) are the major inner ear disorders and frequently present with auditory and vestibular symptoms. This study examined how diving characteristics relate to patterns of auditory trauma. Methods: A retrospective chart review of 30 patients with 36 affected ears was performed. Diving depth, clinical manifestations, and treatment responses were analyzed to identify factors influencing relatively prognosis. Results: Diving depth was the important factor associated with symptom severity and type of injury. Dives deeper than 30 meters of sea water were linked to a higher incidence of sudden sensorineural hearing loss and vertigo. In contrast, transient symptoms with minimal objective abnormalities were typically observed in shallow dives. Patients with concomitant decompression sickness (DCS) showed poorer auditory and vestibular recovery following hyperbaric oxygen therapy, while those without DCS showed better hearing improvement. Vertigo was observed in 80% of IEB cases and 66.7% of IEDCS cases. Hearing recovery was more frequently observed in cases presenting with middle ear symptoms, suggesting a relatively favorable prognosis for IEB compared with IEDCS. Conclusions: Diving depth and DCS involvement may play a role in the severity and prognosis of diving-related inner ear injury. IEB generally demonstrates better auditory outcomes than IEDCS. Further studies with larger cohorts are needed to refine prognostic indicators and optimize management strategies.

Abstract: Background: The continued evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) created ongoing challenges for molecular diagnostics and variant surveillance. Reliable assays capable of maintaining diagnostic sensitivity across emerging variants while providing rapid variant information remain essential for clinical management and public health monitoring. This study evaluated the performance of the GXT96 X3 extraction kit in combination with the FluoroType® SARS-CoV-2 varID Q version 1.0 assay (Hain LifeScience SA (Pty) Ltd, South Africa) for the detection, semi-quantitative assessment, and variant characterization of SARS-CoV-2. Methods: A total of 220 samples were evaluated, including residual nasopharyngeal clinical specimens (n = 183), reference materials, and cultured SARS-CoV-2 virus dilutions. Residual specimens collected during multiple COVID-19 waves in South Africa (wild type, Beta, Delta, and Omicron) were compared against standard-of-care (SOC) molecular assays used for routine diagnosis. RNA extraction was performed using the automated GXT96 X3 platform, followed by amplification on the FluoroCycler® XT using the FluoroType® SARS-CoV-2 varID Q assay targeting RdRp and N genes, with additional spike gene mutation detection for variant identification. Diagnostic accuracy, agreement (Cohen’s kappa), precision, linearity, and limit of detection (LoD) were assessed. Results: The assay demonstrated a sensitivity of 98.4% (95% CI: 94.2–99.8) and specificity of 100% (95% CI: 95.9–100.0) compared with SOC assays, with an overall agreement of κ = 0.981. Precision analysis showed acceptable reproducibility with standard deviation ≤1.49 and coefficient of variation ≤3.83%. Regression analysis demonstrated strong linearity across dilution series (R² = 0.9882 for RdRp and 0.994 for N genes). The LoD was ≤100 copies/mL for the RdRp gene and 250 copies/mL for the N gene. Variant-associated spike mutations detected by the assay corresponded broadly with epidemiological wave patterns observed in South Africa. Conclusions: The GXT96 X3 extraction platform combined with the FluoroType® SARS-CoV-2 varID Q assay demonstrated high diagnostic accuracy, reproducibility, and reliable SARS-CoV-2 detection across a range of viral loads. The assay additionally provides rapid mutation-based variant information, supporting its utility for routine diagnostic testing and complementary variant surveillance.

Abstract: Castration-resistant prostate cancer (CRPC) survives androgen deprivation, a mechanism widely attributed to autonomous de novo steroidogenesis. Despite the clinical deployment of CYP17A1 inhibitors, the metabolic fidelity of the models underpinning this "tumor-as-gonad" dogma remains controversial. Here, integrating high-resolution liquid chromatography-mass spectrometry with transcriptomics across diverse prostate cancer models, we demonstrate that malignant cell lines universally lack autonomous steroidogenic capacity due to the transcriptional silencing of CYP17A1. Instead, these models operate as high-efficiency precursor "converters" by upregulating HSD3B1 and AKR1C3. Clinical stratification of 844 Prostate Adenocarcinoma patients corroborated this precursor-dependent phenotype. We identify a critical divergence: AR-High tumors rely on oxidative phosphorylation, whereas the transition to an AR-Low state is marked by extensive lineage plasticity. Strikingly, a neuroendocrine plasticity score inversely correlates with AR flux and independently predicts clinical progression (HR=2.41, p=0.024). Our findings redefine CRPC metabolism, dictating a therapeutic shift toward targeting downstream precursor conversion and adaptive lineage plasticity.

Abstract: Chemical recycling of polyolefins is essential to mitigate plastic waste accumulation and promote circular economy strategies. Among the various chemical recycling pathways, catalytic pyrolysis, tandem catalyst systems, ethenolysis, hydrocracking, and hydrogenolysis have emerged as promising approaches for converting polyolefin waste into valuable hydrocarbons, including gaseous, liquid, and solid products. This review provides a survey of recent research on these methodologies, with a particular focus on the production of light gaseous hydrocarbons (C1–C4), bypassing the intermediate pyrolysis oil stage, which is often associated with contaminants and increased processing costs. The novelty of the present work lies in its emphasis on gaseous fractions, in contrast to most existing studies that primarily target oil recovery. Aspects such as catalyst selection, reaction conditions, and product distribution are analyzed. Additionally, the current Technology Readiness Level (TRL) of the studied processes, their relative advantages, limitations, and perspectives for industrial applications are discussed. The analysis highlights catalytic pyrolysis with zeolites as the most mature and scalable technological alternative for manufacture of light compounds directly from polyolefin waste, while tandem catalyst systems and ethenolysis constitute promising but still emerging alternatives for targeted gas production.

Abstract: Agroecology systems integrate social and ecological principles into agricultural practices. Current assessments do not adequately consider animal welfare. This study introduces a new agroecology assessment tool by adding livestock sustainability and animal welfare criteria based on the Five Domains. Through a cross-sectional survey of 14 case studies, we examine how livestock sustainability and animal welfare are integrated into agroecological systems. Surveyors gathered data from farms in Kenya (10), Thailand (1), Italy (1), Vietnam (1), and Mexico (1). Results indicate that certain management practices within agroecological systems, specifically import of feed edible for human consumption, impact sustainability and painful beak trimming and stressful transport, negatively impact animal welfare. These findings highlight the need to strengthen agroecological assessment methods by including sustainability and animal welfare indicators. Doing so can help drive food-system change that improves health, reduces disease risk, and enhances animals’ ecological and social contributions. The paper concludes that better policy and knowledge are essential to improving the wellbeing of both animals and farmers in agroecological systems. The integrated tool could help researchers and farmer organizations improve animal welfare on agroecological farms across different contexts. Better animal welfare could also support the wider adoption and scaling of livestock integration in agroecology.

Abstract: Background: Medical thoracoscopy (MT) represents the gold standard for undiagnosed pleural effusions, traditionally performed in the presence of pleural fluid. Recent technical advances have enabled MT in "dry space" conditions (minimal or absent pleural effusion), raising questions about comparative diagnostic efficacy and safety profiles. Objective: This literature review aims to evaluate diagnostic yield and complication rates between traditional MT performed in patients with current pleural effusion and dry medical thoracoscopy (DMT). Results: MT demonstrates diagnostic sensitivity ranged from 80% to 96.3% and specificity close to 100% for malignant pleural disease and diagnostic accuracy is 99.1% for tuberculous pleuritis. DMT using ultrasound guidance achieves comparable diagnostic yield, with recent studies reporting optimal success rates in pleural access and tissue sampling, and diagnostic sensitivity for malignancy up to 100%. Major complication rates are comparable between MT and DMT, with no significant differences in overall adverse events. Mortality rates remain exceptionally low (≤0.1%) for both approaches. Conclusions: MT remains a highly effective diagnostic tool for pleural diseases. DMT represents a valid and safe alternative in patients without significant pleural effusion, offering comparable diagnostic yield. Although technically more demanding, DMT expands diagnostic possibilities in selected clinical scenarios.

Abstract: Background/Objectives: Lung cancer survival depends on early detection; however, in the Philippines, high radiologist workloads and the anatomical complexity of chest X-rays (CXRs) contribute to missed pulmonary nodules and false-negative diagnoses. This study aims to develop an enhanced deep learning model to improve nodule classification and localization sensitivity. Methods: We propose RNNet-MST, an extension of ResNet-50 that incorporates Multi-Scale Transformer blocks for global context modeling and a custom spatial attention mechanism for attention-based weak localization of disease-relevant regions. The model was trained and evaluated on the NODE21 chest X-ray dataset and compared with a baseline ResNet-50 using classification metrics, with attention maps used for weak localization analysis. Results: RNNet-MST demonstrated improved performance across evaluated metrics relative to the baseline model. Nodule Recall increased from 86.18% to 93.09% (+6.91%), reducing false negatives. Test Accuracy reached 95.16% (+0.51%), and the Nodule F1-Score improved to 91.40% (+1.50%), indicating better detection of small and subtle nodules. Conclusions: The integration of multi-scale transformer features improved classification sensitivity, while the attention mechanism provided weak localization cues that aligned more closely with annotated nodule regions than the baseline. RNNet-MST shows potential as a diagnostic support tool, warranting further validation on larger and more diverse clinical datasets to reduce perceptual errors and facilitate early lung cancer detection in resource-constrained settings.

Abstract: To address the issues of manual operation dependency and low efficiency in tunnel fire research combining computational fluid dynamics (CFD)with deep learning, this paper proposes a multi-agent collaborative framework based on large language models to automate the entire process of inverting fire source characteristics. The framework decomposes the traditional workflow into four specialized agents, namely physical modeling, data governance, model training, and evaluation analysis, which collaboratively execute end-to-end tasks from CFD scenario generation to model deployment. The results demonstrate that the CNN-LSTM model performs optimally. Under a 6 second observation window and 10 meter sensor spacing, the average R² reaches 0.942, representing a 2% improvement over the baseline LSTM model, while the RMSE is reduced by 28.8%. Under sparse deployment with 30 meter spacing, the average R² remains as high as 0.917, validating the effectiveness of integrating spatial feature extraction with temporal modeling. This work provides an efficient technological pathway for intelligent tunnel fire identification and advances the research paradigm from manual optimization to multi-agent system optimization.

Abstract: Modern residential toilets pose a significant biomechanical challenge for older adults with diminished muscle strength, as standard seat heights necessitate excessive joint range of motion (ROM) and compensatory upper-limb reliance. This study evaluated the biomechanical efficacy of a biomimetic Stand-assist Toilet Seat designed to facili-tate sit-to-stand (STS) transitions through a proactive curvilinear trajectory. Thirty community-dwelling older adults were stratified into high-, moderate-, and low-functioning groups based on 30-second Chair Stand Test normative data. A mul-ti-modal assessment framework was employed, integrating MediaPipe-based AI pose estimation for joint kinematics and instrumented armrests with high-precision load cells for kinetic analysis. The results demonstrated that the biomimetic seat signifi-cantly optimized movement efficiency, evidenced by a robust reduction in hip and knee ROM with a large effect size (η²p > .70, p< .001). Kinetic data further revealed sub-stantial upper-limb unloading, with significant decreases in peak arm-support force (Fmax,p=.001, η²p =.35) and cumulative impulse (Iarm,p< .001, η²p =.42). While no signifi-cant interaction was found, a clinical trend (η²p =.17) suggested that low-functioning individuals derived the greatest mechanical advantage from the device. By actively guiding the user’s center of mass toward a biomechanically advantageous "power zone," the biomimetic trajectory minimizes compensatory trunk flexion and armrest reliance. These findings provide evidence-based insights into the role of trajecto-ry-informed assistive technology in enhancing toileting safety and functional inde-pendence for the aging population, particularly those exhibiting signs of possible sar-copenia.

Abstract: Objectives: To assess the prevalence of depressive symptoms and examine their associations with tuberculosis-related knowledge, attitudes, and practices (KAP) among patients with multidrug-resistant tuberculosis (MDR-TB). Methods: A cross-sectional study was conducted among 528 MDR-TB patients. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), with a score of ≥10 indicating clinically relevant depression. KAP domains were assessed using a structured scoring system. Associations were analyzed using Spearman correlation and multivariable logistic regression. Results: The mean PHQ-9 score was 5.32 ± 4.35, and 14.96% of participants (n = 79) had clinically relevant depressive symptoms. Among them, 57 had moderate, 17 had moderately severe, and 5 had severe symptoms. Multivariable analysis showed that higher attitude scores were associated with lower odds of depression (aOR = 0.936; 95% CI: 0.886–0.990; p = 0.02). Higher practice scores were also strongly associated with reduced depression risk (aOR = 0.837; 95% CI: 0.778–0.901; p < 0.001). Knowledge score was not independently associated with depression (p = 0.622). Conclusions: Depressive symptoms are common among MDR-TB patients and are more strongly linked to attitudes and practices than to knowledge alone. These findings highlight the importance of integrating mental health screening and behavioral support into MDR-TB management programs to improve comprehensive patient care.

Abstract: Students who stall in the final stage of their degree rarely do so because they lack technical skill. More often, confidence erodes under sustained uncertainty, motivation shifts from intrinsic engagement to anxious compliance, and the demands of organizing months of research exceed what willpower alone can sustain. This study examines those emotional and motivational dynamics directly, treating research self-efficacy and intrinsic motivation not as background variables but as the affective-motivational core of thesis performance. Using partial least squares structural equation modeling (PLS-SEM) grounded in self-determination theory and social cognitive theory, we tested an integrative model with data from 396 undergraduate students actively completing theses at public and private universities in the northern region of Peru. Four enabling factors — methodological competencies, intrinsic motivation, tutorial support, and resources and conditions — were linked to thesis quality and process efficiency through two mediating mechanisms: research self-efficacy (the confidence to face methodological difficulty without retreating) and project management (the behavioral self-regulation that converts motivation into organized work). Resources and conditions showed the strongest associations in the model, with the largest effects on both project management (β = 0.533) and research self-efficacy (β = 0.418). Self-efficacy, in turn, was the primary predictor of thesis quality (β = 0.518), while project management and quality together drove process efficiency. The model explained 70.5% of variance in thesis quality and 81.4% in process efficiency. These pa

Abstract: Dental pain due to dentine hypersensitivity or pulpitis is characterized by short or lasting episodes of pain triggered by normally innocuous stimuli originating from exposed dentine. Both represent the most frequent pain of the orofacial region. Transient receptor potential (TRP) superfamily of ion channels participates in the detection of different modalities of sensibility in the mammalian sensory teeth system, i.e., trigeminal neurons and odontoblasts. In particular, some members of the melastatin family (TRPM) serve as molecular thermal sensors, and temperature is one of the most potent stimuli in triggering dentine hypersensitivity. Here we review and update the information about the distribution of TRPM channels in the trigeminal ganglion and dental pulp cells, especially odontoblast, in humans and animal models. In addition to the well know sensory roles of TRPM, other functions such as in development and mineralization of teeth are considered.

Abstract: Plastic packaging waste has emerged as a critical environmental challenge due to its persistence, low degradation rates, and increasing accumulation in terrestrial and marine ecosystems. Conventional petroleum-based plastics dominate packaging applications because of their durability and low cost; however, their environmental impacts have prompted urgent demand for sustainable alternatives. Bio-based and compostable packaging materials offer promising solutions by utilizing renewable resources and enabling environmentally benign end-of-life pathways. This paper examines the development of bio-based and compostable packaging alternatives aimed at reducing plastic waste. Through a systematic review of material innovations, processing technologies, and life-cycle considerations, the study evaluates the performance, environmental benefits, and limitations of emerging bio-based packaging solutions. The findings indicate that materials such as polylactic acid, polyhydroxyalkanoates, starch-based composites, and cellulose-derived packaging can significantly reduce fossil resource dependency and plastic pollution when supported by appropriate infrastructure. The paper concludes that while bio-based and compostable packaging presents strong environmental potential, successful large-scale adoption requires integrated design strategies, composting infrastructure, and supportive policy frameworks.

Abstract: Distributed modern software platforms spanning microservices, serverless functions, and edge computing face unprecedented security threats from stealthy adversaries exploiting encrypted data flows and behavioural camouflage. Conventional defences require decryption for analysis, exposing sensitive information in untrusted cloud environments. This paper proposes an innovative framework integrating homomorphic encryption (HE) with automated threat hunting to enable privacy-preserving threat detection at scale. Using levelled BFV schemes from OpenFHE, we perform computations directly on ciphertexts for anomaly scoring and behavioural profiling, while our hunting engine employs graph neural networks and isolation forests to hypothesize and pursue attacker patterns across distributed logs without plaintext exposure.The architecture deploys as Kubernetes-native operators, processing 10,000 encrypted events per second with 92% detection accuracy on MITRE-emulated scenarios, outperforming traditional UEBA by 35% in F1 score and reducing analysis latency from hours to seconds. Evaluations on AWS EKS clusters demonstrate sub-200ms query times for homomorphic aggregations, with noise management via bootstrapping optimizations. Case studies in fintech pipelines reveal thwarted supply-chain compromises and insider data exfiltration’s. By revolutionizing secure computation in dynamic ecosystems, our solution bridges cryptography and AI-driven hunting, offering deployable resilience against evolving threats while complying with GDPR and zero-trust mandates. Future work extends to fully homomorphic deep learning for adaptive adversary modelling.

Abstract: Avian influenza is a critical zoonotic infection threatening both the poultry industry and global public health. While traditional intramuscular vaccines elicit systemic im-munity, they often fail to provide robust local protection at mucosal surfaces. There is thus significant interest in the development of mucosal avian influenza vaccines administered via the intranasal route. However, in humans, this approach is significantly hampered by the availability of safe and effective adjuvants. This study investigated the immunogenic-ity of a modified recombinant influenza A/H5 hemagglutinin (rHA/H5-modif) formulated with Novochizol, a novel chitosan-derived delivery system, administered intranasally to laboratory animals. Our results demonstrate that mucosal immunization with the rHA/H5-modif/Novochizol complex induces potent humoral (IgG and IgA) and cell-mediated immune responses. Crucially, the formulation provided 100% survival in mice following a lethal challenge with highly pathogenic avian influenza A/H5. These findings position the rHA/H5-modif/Novochizol complex as a promising candidate for next-generation mucosal vaccines, in particular against highly pathogenic avian influen-za A/H5 subtype.