Abstract: The Next-Generation Security Triad—integrating post-quantum cryptography (PQC), Zero Trust Architecture (ZTA), and AI security—provides comprehensive protection for autonomous sensing systems. However, existing frameworks assume enterprise connectivity is available in tactical environments operating under Disconnected, Intermittent, and Low-bandwidth (DIL) conditions. This paper presents the Tactical Edge Triad Architecture (TETA), adapting enterprise substrate components for disconnected operations through five modules: Edge Cryptographic Module (ECM), Tactical Identity Cache (TIC), Edge Analytics Engine (EAE), Mission Policy Store (MPS), and the Autonomous AI Governance Framework (AAGF). Three mechanisms address DIL-specific challenges: Authority Decay provides a DIL-specific operationalization of continuous verification through progressive privilege reduction with formal attack mitigations; Pre-Mission Consensus Packaging provides cryptographically signed governance envelopes satisfying human oversight requirements; and Triad Integration demonstrates cross-pillar security dependencies. The AAGF systematically adapts established governance mechanisms, behavioral envelopes, watchdog models, autonomy-downgrade, and consensus-backed approval for disconnected operations. Analytical evaluation across two tactical scenarios demonstrates feasibility: PQC overhead estimates derive from published pqm4 benchmarks; governance function estimates (policy evaluation, watchdog inference, audit logging) are engineering projections based on comparable embedded workloads. Combined governance latency is estimated at ~15 ms on Cortex-A53 class processors (±40%), with 0.5% steady-state bandwidth increase for PQC. TETA enables Triad implementation at the tactical edge while preserving security properties and governance accountability.

Abstract: This article examines the development of operator splitting methods in Soviet numerical analysis during 1955–1975, with particular focus on N.N. Yanenko’s formalization of the Method of Fractional Steps at the Siberian Branch of the USSR Academy of Sciences. While similar techniques were independently developed in the West (Peaceman-Rachford 1955, Douglas-Rachford 1956), the Soviet school pursued a distinct trajectory shaped by acute hardware constraints and deep epistemological commitments to operator theory. Through analysis of technical publications, archival materials, and comparative historiography, this study argues that material scarcity catalyzed a systematic research program emphasizing computational economy, while a pre-existing mathematical culture valorizing theoretical elegance reinforced this trajectory. The case illuminates how geopolitical constraints and intellectual traditions jointly shaped algorithmic innovation, contributing to methods that ironically became foundational for modern massively parallel computing. Significant archival gaps limit definitive claims about industrial applications, highlighting the need for further primary source research.

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Background: The incidence of patients with nontuberculous mycobacterial pulmonary disease (NTM-PD) complicated by chronic pulmonary aspergillosis (CPA) has been increasing. CPA is known to be associated with complex treatment regimens and a poor prognosis. However, data from mainland China remain scarce. Objective: This single-center retrospective study aimed to evaluate the clinical characteristics, risk factors, and prognoses of patients with NTM-PD who were coinfected with CPA. Methods: We conducted a retrospective review of the medical records of 248 patients diagnosed with NTM-PD. Risk factors for CPA were analyzed via multiple logistic regression, followed by survival analysis. Results: Among the 248 patients with NTM-PD, 66 (26.6%) were diagnosed with CPA. Independent risk factors for NTM-PD and CPA coinfection included male sex(OR 2.13, 95% CI:1.03-4.47), dyspnea(OR 27.9, 95% CI:4.24-570), cavity(OR 5.95, 95% CI:2.76-13.9), use of oral corticosteroids(OR 4.28, 95% CI:1.13-16.6), and interstitial lung disease(OR 15.5, 95% CI:1.89-361). The Kaplan-Meier survival curves indicated a significant divergence between the NTM-PD group and the NTM-PD with CPA group (log-rank test, p = 0.00039; HR 2.01, 95% CI:0.66-6.12). Conclusion: In patients with NTM-PD, the presence of concurrent CPA is associated with a marked increase in mortality. Clinicians should maintain a high index of suspicion for CPA to ensure prompt diagnosis and treatment, particularly in high-risk individuals.

Abstract: Stroke has been a topic of extensive research due to its debilitating consequences and high mortality. New findings offer a deeper understanding of specific factors that affect post-stroke recovery and identify therapies that may facilitate this process. One such factor was microglia, neuronal immune cells that are highly reactive to cytokines in the neuroenvironment and can, in turn, affect the inflammatory cascades that originate after stroke, making them ideal candidates for immunomodulation in the brain. Many FDA-approved immunotherapies have been found to target distinct inflammatory signaling molecules and responders, including IL-6 inhibitors, IL-13 inhibitors, IL-12/IL-23 inhibitors, B-cell modulators, Type I interferon inhibitors, CAR T-cell therapy, Calcineurin inhibitors, Complement inhibitors, and JAK-STAT pathway inhibitors. The FDA-approved immunotherapies discussed in this review demonstrate potential in modulating the immune response after stroke by targeting key inflammatory pathways involved in secondary brain injury. Future research should focus on defining optimal therapeutic windows, identifying suitable patient populations, determining the most appropriate timing of therapy, and targeting specific immune mechanisms to balance the attenuation of harmful inflammation with the preservation of reparative processes.

Abstract: Poly-γ-glutamic acid (γ-PGA) is an important biopolymer produced by various species of Bacillus. Novel γ-PGA producers have shown strain-dependent nutritional and culture requirement that must be characterised and optimised to improve γ-PGA yields. The optimal nutritional and cultural condition for maximum γ-PGA titre in a newly identified γ-PGA producing strain Bacillus licheniformis DPC6338 was determined using one factor at a time (OFAT) and design of experiments (DOE). The optimal nutritional and culture condition for maximum γ–PGA titre in B. licheniformis DPC6338 was 67g/L glutamic acid, 32g/L tryptone, 75g/L glucose, 5g/L citric acid, 2g/L K₂HPO₄, 0.5g/L MgSO₄·7H₂O, 0.02 g/L FeCl₂·4H₂O, 0.1g/L CaCl₂·2H₂O, 0.5 g/L MnSO₄·H₂O, 2g/L ZnSO₄·7H₂O, 42°C, pH 6.5 – 7.0, 1% inoculum, at 250 rpm. Under optimised conditions in shake flask, maximum γ–PGA titre 75.35 ± 0.38 was obtained after 96h while peak productivity of 1.3 g/L/h occurred at 48 h, representing a 27% and 4% improvement in titre and productivity compared to the screening medium. Scale-up to bioreactor conditions significantly enhanced the final titre γ-PGA and early-phase volumetric productivity by ~30% and ~80% respectively. The results obtained in this study highlight the potential of B. licheniformis DPC6338 for industrial γ-PGA producing strain.

Abstract: Financial statement fraud continues to pose a significant challenge to audit effectiveness, investor confidence, and the integrity of financial markets. Fraud detection is particularly complex due to the highly imbalanced nature of financial reporting data, where fraudulent observations constitute only a small fraction of the total sample. In such settings, conventional accuracy-based evaluation often produces misleading conclusions and fails to reflect practical audit value. This study conducts a comparative evaluation of four deep learning models, namely LSTM, GRU, CNN1D, and Transformer, for financial statement fraud detection under class-imbalanced conditions. The analysis is based on a dataset of 805 firm-year observations. It adopts Precision–Recall Area Under the Curve as the primary performance metric, complemented by ROC-AUC, Precision, Recall, F1 score, and Specificity. To assess practical usability, Decision Curve Analysis is employed to evaluate the decision-level net benefit of each model across different threshold probabilities, and bootstrap resampling is used to assess performance stability under random data partitioning. The empirical results show that the Transformer model consistently outperforms the other architectures in terms of discriminative ability, robustness, and decision-level utility. Its attention-based structure enables effective modeling of global relationships among financial indicators, leading to stable performance across varying thresholds and data splits. The CNN1D model demonstrates relatively high specificity and a balanced error structure, suggesting its suitability in audit environments where minimizing false positives and controlling verification costs are critical. In contrast, although the LSTM and GRU models exhibit higher sensitivity to fraudulent cases, their lower precision and stability limit their effectiveness as standalone solutions. Overall, the findings emphasize the importance of imbalance-aware, decision-oriented evaluation frameworks for detecting financial statement fraud. The study offers practical insights for auditors and regulators by identifying deep learning models that combine statistical reliability with operational relevance in real-world auditing contexts.

Abstract: Temporal reasoning is an important part of the field of time geography. Recent advances in qualitative temporal reasoning have developed a set of 74 relations that apply between discretized time intervals. While the identification of specific relations is important, the field of qualitative spatial and temporal reasoning relies on conceptual neighborhood graphs to address relational similarity. This similarity is paramount for generating essential decision support structures, notably reasonable aggregations of concepts into single terms and the determination of nearest neighbor queries. In this paper, conceptual neighborhoods graphs of qualitative topological changes in the form of translation, isotropic scaling, and anisotropic scaling are identified using a simulation protocol. The outputs of this protocol are compared to the extant literature regarding conceptual neighborhood graphs of the Allen interval algebra, demonstrating the theoretical accuracy of the work. This work supports the development of robust spatio-temporal artificial intelligence as well as the future development of spatio-temporal query systems upon the spatio-temporal stack data architecture.

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We devised a quantitative scoring function to assess the cumulative effects of nonsynonymous single nucleotide variants (SNVs) on protein-coding genes in patients with ovarian cancer (OvCa) and thyroid cancer (ThCa). The goal is to find novel candidate cancer-related genes for downstream bioinformatics analyses and wet-lab studies. With Genomic Data Commons as primary data resource, SNV information was extracted from whole-exome sequencing data from patients with these cancers. A cumulative variant scoring function, Q(G) was developed to sum up the deleterious effects of the individual SNVs on the gene G. While Q(G) can be computed using any popular functional effect analyzers such as FATHMM-XF, SIFT, PolyPhen, and CADD, we have also established an integrative scoring function iQ(G) that combines the deleterious assessments from different analyzers and demonstrated that iQ(G) is a more effective method for identifying likely cancer-related genes. Based on the iQ(G) rankings, the top three novel genes for OvCa are AHNAK2, UNC13A, and PCDHB4; and those for ThCA are PLEC, HECTD4, and CES1. Furthermore, the top 1% genes with highest iQ(G) scores for each cancer were submitted for KEGG pathway analysis. The results revealed that several genes of the CACNA1 family within the type II diabetes mellitus pathway are likely related to both OvCa and ThCa and suggested other molecular interactions that should be further studied in connection with OvCa prognosis and ThCa treatment.

Abstract: The Indonesian archipelago represents one of the most tectonically complex regions on Earth, where the convergence and interaction of multiple plates drive ongoing subduction, arc-continent collision, and lithospheric accretion. To unravel the detailed structure and dynamics of this convergent margin, we develop a novel, high-resolution 3-D shear-wave velocity model of the lithosphere and upper mantle. This model is derived from a weighted joint inversion of complementary surface-wave datasets: teleseismic Rayleigh waves from 387 shallow earthquakes (MS ≥ 5.5) recorded across 31 stations, analyzed using a modified two-plane-wave tomography method, and ambient-noise correlations from two years of continuous data at 30 stations, processed with far-field approximation and image-transformation techniques. This integrated approach significantly enhances the resolution of shallow structures compared to previous body-wave tomographic models. Our model provides new insights into the four primary subduction systems. Along the Sunda-Java trench, we document a systematic along-strike transition in slab geometry: a continuous, well-defined slab in the west progressively gives way to increasingly disrupted and thickened structures eastward. This morphological evolution correlates with the subduction of progressively older oceanic lithosphere and is influenced by variations in slab age, dip, and the presence of deep slab tearing. Beneath the Banda Arc, we image an approximately 200 km-thick slab and attribute its dramatic 180° curvature to the mechanical interaction between the northward-subducting Australian plate and a distinct south-directed subduction system beneath the Seram region. In the Molucca Sea, our high-resolution tomography reveals a shallow (~50 km depth) low-velocity zone and details the complex geometry of an active double-sided subduction zone, characterized by asymmetric dips and intense seismicity, which illuminates the dynamics of ongoing arc-arc collision. Finally, beneath the Celebes Sea, a south-dipping slab is clearly resolved under North Sulawesi, while no substantial subduction signature is associated with the Sangihe Arc. Collectively, these findings provide unprecedented structural constraints on the segmentation, deformation, and interaction of subducting slabs in Indonesia. They underscore the control of lithospheric age and complex plate interactions on slab morphology and regional tectonics, offering a refined framework for understanding the geodynamic evolution of this exceptionally complex convergent boundary.

Abstract: Addiction is a neuropsychiatric disorder characterised by compulsive substance use despite harmful consequences. Ketamine a dissociative anaesthetic increasingly misused among young people has become a global public health concern, necessitating the search for effective neuroprotective interventions. N-acetylcysteine (NAC) a glutathione precursor with antioxidant and anti inflammatory properties, has shown promise in mitigating substance-induced neurotoxicity. This study investigated the neuroprotective effects of NAC on ketamine induced cerebellar alterations in Wistar rats. Sixty adult Wistar rats (120–150 g) were randomly assigned to six groups. Group A received distilled water (control); Groups B and C received NAC (500 or 1000 mg/kg, orally) Group D received ketamine (15 mg/kg, intraperitoneally) while Groups E and F received ketamine followed by NAC (500 or 1000 mg/kg, respectively). Ketamine was administered for 10 days followed by NAC treatment from days 11 to 24. Behavioural assessments including open-field Y-maze, and catalepsy tests, were conducted on day 25. Animals were then euthanised for biochemical analyses of total antioxidant capacity (TAC) malondialdehyde (MDA), tumour necrosis factor-alpha (TNF-alpha), and interleukins IL-1 beta, IL-6, and IL-10. Cerebellar tissues were processed for histological evaluation. Ketamine exposure induced hyperlocomotion, increased rearing, working memory deficits, oxidative stress, and elevated pro-inflammatory cytokines, with a concomitant reduction in anti-inflammatory markers. NAC treatment at both doses significantly attenuated these behavioural and biochemical disturbances. Histological examination revealed marked cerebellar neurodegeneration, including Purkinje and granule cell loss, in ketamine-treated rats, whereas NAC particularly at 1000 mg/kg largely preserved cerebellar cytoarchitecture. In conclusion, NAC exerted significant neuroprotective effects against ketamine-induced behavioural, biochemical, and structural cerebellar damage in rats, supporting its potential therapeutic relevance in mitigating ketamine-related neurotoxicity.

Abstract: Modern high-speed train compartments contain intricate internal configurations. In the event of a fire emergency, the propagation velocity of flames through the passenger cabin is determined by multiple factors, including compartment design, ignition source characteristics, and airflow conditions. This study employed numerical simulation approaches to investigate the effects of fire source power, fire source location, and longitudinal ventilation velocity on the rate of flame progression. The simulation outcomes reveal that, under forward ventilation conditions, the magnitude of fire power has a minimal influence on flame propagation speed. However, stronger fire sources lead to earlier initiation of flame spread along the carriage. Central positioning of the ignition source results in bidirectional flame movement toward both ends of the carriage, with faster propagation rates than those of fires originating at the extremities. Longitudinal airflow patterns significantly. When the airflow speed within the tunnel remains below 3 meters per second, the impact of longitudinal ventilation on fire propagation speed in the train is minimal under forward ventilation conditions. Conversely, in reverse-ventilation scenarios, the rate of flame advancement shows a positive correlation with increasing ventilation speed. Nevertheless, once tunnel ventilation velocities exceed 3 m/s, combustion propagation within high-speed rail carriages becomes impossible due to intact windows, which create oxygen-deficient conditions that prevent the development of fire.

Abstract: Melanoma, a highly aggressive and metastatic cancer, poses significant challenges due to its propensity to spread to distant organs, with brain metastasis representing a particularly devastating complication. This review synthesizes recent advances in understanding the molecular, cellular, and microenvironmental mechanisms driving melanoma metastasis, with a specific emphasis on brain metastasis. We explore the unique challenges of brain metastasis, including blood-brain barrier penetration, brain-specific microenvironment interactions, and genomic distinctions. Advances in diagnostic tools, such as imaging and liquid biopsies, are discussed alongside current and emerging therapeutic strategies, including novel small molecules, immunotherapies, and combination approaches tailored for brain metastases. The review also highlights the immunological landscape of the brain, translational models, and multidisciplinary clinical management strategies. Finally, we identify critical research gaps, including the need for brain metastasis-specific clinical trials, AI-driven predictive models, and preventive strategies, to guide future efforts in improving outcomes for patients with melanoma brain metastasis.

Abstract:

Background/Objectives: Carfilzomib (CFZ) and bortezomib (BTZ) are proteasome inhibitors used as the first-line therapy for relapsed or refractory multiple myeloma (MM) but are associated with cardiovascular adverse events (CVAEs). This study aims to identify differentially methylated positions (DMPs) and regions (DMRs), and enriched pathways in patients who developed CFZ- and BTZ- related CVAEs. Methods: Baseline germline DNA methylation profiles from 79 MM patients (49 on CFZ and 30 on BTZ) in the Prospective Study of Cardiac Events During Proteasome Inhibitor Therapy (PROTECT) were analyzed. Epigenome-wide analyses within each group identified DMPs, DMRs, and enriched pathways associated with CVAEs compared with individuals without CVAEs. Results: Four DMPs were significantly associated with CFZ-CVAE: cg15144237 within ENSG00000224400 (p = 9.45x10⁻¹⁰), cg00927646 within TBX3 (p = 9.78x10⁻⁸), and cg10965131 within WDR86 (p = 1.00x10⁻⁷). One DMR was identified in the FAM166B region (p = 5.46x10⁻⁷). There was no evidence of any DMPs in BTZ-CVAE patients, however two DMPs and one DMR reached a suggestive level of significance (p < 1.00x10⁻⁵): cg09666417 in DNAJC18 (p = 3.41x10⁻⁷) and cg12987761 in USP18 (p = 5.00x10⁻⁷), and a DMR mapped to the WDR86/WDR86-AS1 region (p = 8.11x10⁻⁸). Meta-analysis did not find any significant DMPs, with the top CpG being cg17933807 in GNL2 (p = 7.38 x10⁻⁵). Pathway enrichment analyses identified peroxisome, MAPK, Rap1, adherens junction, phospholipase D, autophagy, and aldosterone-related pathways to be implicated in CVAEs. Conclusions: Our study identified distinct DMP, DMR, and pathway enrichment associated with CVAE, suggesting epigenetic contributors to CVAEs and supporting the need for larger validation studies.

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Dengue has emerged as one of the most severe and rapidly escalating public health threats in Bangladesh, reflecting both localized vulnerabilities and broader global transmission dynamics. This study aims to examine the key environmental, climatic, and socioeconomic drivers underlying the country’s unprecedented dengue surge since 2018, with particular emphasis on post-COVID trends. The central research questions are: (i) how climate variability and urban environmental changes are reshaping dengue transmission in Bangladesh, (ii) which often-overlooked structural factors are intensifying the severity of outbreaks, (iii) how these local dynamics reflect emerging global risks, and (iv) how global risk management practices can be effectively implemented in the Bangladeshi context. Using a comprehensive narrative review of national surveillance data obtained from official sources, peer-reviewed literature, meteorological records, and validated public reports, the study synthesizes evidence on temperature rise, altered rainfall patterns, humidity, unplanned urban growth, population density, sanitation failures, construction activity, pollution, insecticide resistance, and declining green cover. Findings indicate that dengue transmission in Bangladesh is driven by a convergence of climate stressors and human-made environmental conditions, particularly clogged drainage systems, unmanaged plastic waste, water storage practices, and high-rise construction sites that facilitate Aedes mosquito breeding. The study concludes that Bangladesh’s dengue crisis represents an early warning of a wider global emergency. Addressing it requires integrated climate-responsive surveillance, urban planning reforms, strengthened vector control, and coordinated public health action grounded in a One Health approach.

Abstract: In this article the relationships are revealed between the views on neutrinos as they show up in various approaches of study. Among these are (a) Fermi’s theory on beta decay, (b) the classical view on the decay of the pion into a muon and a muon neutrino, (c) instrumental attempts for direct measurements of the neutrino’s rest mass like in the KATRIN project, (d) the studies in modern neutrino observatories on the phenomenon of neutrino oscillation and (e) the view on neutrinos in the Structural Model of particle physics. A non-classical kinematic analysis on lab frame decay processes shows that the effective masses of the three neutrinos are the same, although in this respect the comparison with the present data in the PMNS theory is not fully conclusive. Adopting the hypothesis that neutrinos fly at the lab frame speed of pions in free flight, their rest masses have to be set at about 80 meV/c².

Abstract: As a high-yield and fast-growing novel forage, Juncao (Cenchrus fungigraminus) holds significant potential for feed applications. Appropriate processing methods can effectively enhance the feeding efficacy of Juncao silage and reduce feed costs for farmers and herdsmen. In this study, Juncao at three different heights (1.0–1.5 m, 1.5–2.0 m, and 2.0–2.5 m) was selected for silage fermentation to determine the optimal harvesting height. Additionally, Juncao at a height of 2.5–3.0 m, which possesses the highest cellulose content, was selected for cellulose degradation analysis to evaluate the degradation efficiency of conventional silage additives on fiber content.The results indicated that the fiber content of Juncao silage was significantly positively correlated with growth height, whereas crude protein and crude fat contents showed a significant negative correlation. Furthermore, the total volatile fatty acid (TVFA) and lactic acid contents reached their peak in the 2.0–2.5 m (High) group. Cellulose degradation analysis revealed that the degradation rates of various cellulose components were higher under natural fermentation conditions compared to treatments with silage additives. However, further research is required to explore whether specific additives tailored for Juncao silage exist. Based on this experimental analysis, it can be concluded that utilizing 2.0–2.5 m Juncao for natural fermentation during the ensiling process can effectively improve nutritional composition and fermentation quality while achieving a higher cellulose degradation rate. Nonetheless, subsequent studies are necessary to refine and establish a complete and scientific methodology for Juncao silage production.

Abstract: Traditional electromagnetism quantifies the modulation of electromagnetic fields by media through permittivity (ε) and permeability (μ), yet there remain points worthy of discussion in the explanation of the microscopic mechanism, such as the understanding of vacuum attributes and the essence of the action mechanism. The Theory of Existence Field proposes that fundamental physical quantities (charge/mass) possess an inherent property of diffusing their own physical information into space, and the resulting "existence field" serves as the carrier of physical interaction. Based on the Theory of Existence Field and combined with the Unified Theory of Atomic and Molecular Structure (where the spatial configuration of electron orbitals determines atomic magnetic moment), this paper systematically deduces the microscopic mechanisms of dielectric polarization and magnetization. The research elucidates that the essence of dielectric electromagnetic effects is that an external source existence field transmits physical information to the charges within the medium; the charges respond to the information, generating directional force effects (charge displacement/magnetic moment reorientation), which then form macroscopic effects through the superposition of microscopic existence fields; Permittivity is a quantitative representation of the internal charges' response to external charge information, producing polarization effects, while permeability is a quantitative representation of atoms containing unpaired electron orbitals responding to external magnetic information, producing magnetization effects. Through the deduction using parallel plate capacitor and magnetic medium models, this theory provides a mechanism-clear microscopic explanation for dielectric electromagnetic phenomena, offering a new theoretical framework for related research.

Abstract:

The classical Rankine and Coulomb theories frequently encounter difficulties in accurately modeling the complex, nonlinear, and displacement-coupled behavior of earth pressure on retaining walls under non-limit states. The present study proposes a “key feature refinement strategy based on collinearity analysis” and employs the said strategy by applying it to model test data. The strategy identified an optimum set of five physical parameters, namely displacement mode (DM), relative displacement (Δ/H), relative depth (Z/H), unit weight (γ), and internal friction angle (φ). A machine learning (ML) model has been developed that integrates Categorical Boosting with SHapley Additive exPlanations (CatBoost-SHAP). This model has been found to exhibit a marked enhancement in accuracy (R² = 0.917) when compared to classical theories, while concurrently offering the distinct advantage of explicit interpretability. SHAP analysis has been demonstrated to elucidate the nonlinear influence of each parameter. It is confirmed that displacement mode is identified as the governing factor for spatial pressure distribution, and classical mechanisms such as top‑down stress relaxation in the rotation-about-the-base (RB) mode and soil arching in the rotation-about-the-base (RT) mode are visualized. Furthermore, a displacement‑dependent mechanical threshold (Δ/H ≈ 0.006) has been identified, which marks the transition from a mode‑dominated to displacement‑driven pressure evolution. In addition, the proposed approach is integrated into a graphical user interface (GUI) that is designed to be user‑friendly, thereby furnishing practitioners with a precise tool for designing retaining walls. The validation of the model's performance against independent experimental results has demonstrated its superior agreement and practical utility under displacement-controlled conditions in comparison to conventional methods.

Abstract: The close physiological relationship between the left atrium (LA) and left ventricle (LV) suggests that an index assessing both the cardiac chambers simultaneously could provide useful information about disease severity. Consequently, investigators have proposed the atrioventricular coupling index (LACi), and demonstrated its utility in predicting the likelihood of atrial fibrillation, heart failure, and other cardiovascular events in humans. No studies have been reported in veterinary medicine. Therefore, we measured the LACi in healthy dogs and dogs affected by myxomatous mitral valve disease (MMVD). Two hundred and thirty-three dogs (105 healthy dogs and 128 dogs with MMVD) were retrospectively included in the study. The LACi (LA volume/LV volume*100) at LV end-diastole (LACi-ED) and LV end-systole (LACi-ES) of each dog was measured using a monoplane Simpson’s Method of Discs from the left apical four-chamber view. In healthy dogs, LACi-ED and LACi-ES showed no relationship with bodyweight, heart rate and age (R2 &lt; 0.03, for all variables). In MMVD dogs, LACi-ED and LACi-ES differed between ACVIM stages (P &lt; 0.00 and P &lt; 0.02, for all stages). The LACi-ED and LACi-ES had similar accuracy in identifying MMVD dogs with congestive heart failure (area under the curve of 0.920 and 0.906, respectively). Our data suggest that LACi can be useful in assessing left atrioventricular function in dogs with MMVD but the diagnostic accuracy in identifying dogs with congestive heart failure was not superior to left-atrial-to-aortic ratio. Prospective studies are needed to evaluate the predictive value of this new echocardiographic index in dogs affected by MMVD.

Abstract: This paper presents an edge-reinforced learning platform that combines reinforcement learning, homomorphic encryption, and swarm intelligence to support ultra-low latency IoT sensing and cross-device communication. In conventional IoT architectures, cloud-centric processing and centralized coordination introduce significant delays and expose sensitive data to intermediate entities, making them unsuitable for time-critical and privacy-sensitive applications. The proposed platform relocates intelligence to the network edge, where edge nodes learn adaptive policies for sensing, routing, and computation offloading based on local conditions and limited global feedback. To preserve confidentiality, IoT measurements and model updates are protected using homomorphic encryption, allowing aggregation and decision-making to be performed directly over encrypted data without revealing raw values. In parallel, swarm intelligence mechanisms orchestrate distributed cooperation among devices, enabling robust path selection, task allocation, and congestion avoidance through lightweight, bio‑inspired interactions rather than centralized control. The integrated design is evaluated on realistic IoT scenarios with heterogeneous devices and dynamic traffic patterns. Results show that the edge-reinforced learning platform can significantly reduce end-to-end latency and jitter compared to cloud-based and non-learning edge baselines, while incurring acceptable computational overhead from encryption and maintaining strong privacy guarantees. The framework demonstrates that it is feasible to simultaneously achieve low latency, resilient cross-device coordination, and data confidentiality in large-scale IoT deployments.