Abstract: Quantum gravity is often probed by thought experiments that assume unlimited classical control over quantum systems. The firewall paradox is one such thought experiment. It asks if one observer can decode early Hawking radiation, and then later compare with degrees of freedom behind the horizon. Here I show that this protocol is constrained by an instruction budget. If the correct decoding depends on the black hole microstate, a hidden internal configuration, then the observer must specify which decoder to use. The number of decoders needed to cover all microstates grows exponentially with the black hole entropy, so the decoder specification requires a number of bits that scales with that entropy. A covariant entropy bound limits how much information can be stored within a bounded causal patch. This enforces a horizon-scale lower bound on the region required for single-observer verification, even when computational complexity is ignored. More broadly, the result highlights a principle for quantum gravity experiments. Control information is physical, and bounds on storage can decide what is testable.

Abstract: This paper presents a comprehensive diagnostic framework for electrical machines, based on the application of artificial neural networks (ANNs) for the analysis of electrical and vibration signals. The proposed method leverages deep learning architectures to automatically extract informative features and achieve high fault classification accuracy. The framework integrates signal pre-processing, neural network training, and a condition evaluation module, enabling the implementation of a predictive maintenance system suitable for industrial applications. A multi-sensor diagnostic system is proposed, combining CNN-LSTM architectures with a graph neural network (GNN) for correlational analysis of currents, vibrations, and thermal parameters. This approach allows early detection of inter-turn short circuits and bearing faults, improving diagnostic accuracy by 7–12% compared to existing state-of-the-art methods. The framework demonstrates robustness under varying operating conditions, including transient and self-excitation regimes, and provides physically interpretable results, bridging the gap between data-driven and physics-informed diagnostics.

Abstract: Background: Accelerating global population aging underscores the need to identify multidimensional determinants of successful aging. This review synthesizes evidence on social, spiritual, and religious factors that shape well-being, and quality of life in later adulthood. Successful aging is conceptualized as adaptation to age-related challenges through internal and external resources. Emerging research suggests that pet ownership and companion animals promote meaning, purpose, and social connectedness, while spiritual, religious, and contemplative practices support coping, psychological stability, and foster life satisfaction in older adults. Methods: This review of the literature examines the intersection of spirituality, religious practice, meditation, life purpose, and pet ownership as mediating and reinforcing influences on successful aging. This review focused on evidence linking prayer, meditation, chanting, and spiritual and religious participation to psychological, cognitive, and physiological outcomes, as well as literature exploring human-animal relationships in later life. Results: Findings indicate that spiritual and religious practices, companion animal relationships, and contemplative practices support core aspects of successful aging, including emotional well-being, reduced loneliness, enhanced coping, and greater life meaning. Yet, it remains unclear whether these influences act synergistically or independently, and how they shape older adults’ experiences of aging and adaptation to decline. Conclusions: Incorporating spiritual, religious, and contemplative practices alongside companion animals may enhance holistic models of successful aging by supporting emotional, social, and spiritual well-being. Future research should explore multidimensional mechanisms to inform interventions that improve quality of life in later adulthood.

Abstract: Extracellular vesicles (EVs) are lipid bilayer–bound structures capable of transporting molecular markers from their cell of origin and are secreted by multiple cell types, including malignant cells. EVs have emerged as promising tools for developing less invasive diagnostic approaches. In B-cell acute lymphoblastic leukemia (B-ALL), immunophenotypic characterization of extracellular vesicle–enriched populations (EVEPs) in peripheral blood (PB) may provide complementary information for disease detection and monitoring. This exploratory study aimed to characterize EVEPs obtained from peripheral blood (PB) and bone marrow (BM) of adult patients with B-ALL and to compare them with the clinical immunophenotype (CIP). EVEPs were isolated by differential centrifugation and analyzed by flow cytometry and confocal microscopy, primarily evaluating CD3 and CD19 expression. EVEPs derived from PB samples of patients with B-ALL showed increased expression of B-lineage markers (CD45, CD34, CD19, CD20, and CD10), consistent with the leukemic phenotype identified in the CIP. Additionally, CD3⁺CD19⁺ EVEPs were occasionally detected. These findings suggest that EVEPs partially reflect the leukemic immunophenotype and may serve as a complementary source of biological information. The detection of CD3⁺CD19⁺ events highlights complex cellular interactions within the leukemic niche and warrants further investigation.

Abstract: Streaming Transformer Networks: Unified Hearing-to-Speech Recognition and Intelligent Text Generation Systems introduce a groundbreaking architecture that processes real-time audio streams to produce both synthesized speech outputs and contextually intelligent text, overcoming traditional limitations in multimodal AI systems. Traditional speech recognition models often operate offline, requiring full audio sequences before generating results, which hinders interactive applications. This work proposes a transformer-based framework that unifies hearing-to-speech translation directly converting input audio into natural-sounding speech with advanced text generation capabilities, enabling seamless dual-mode responses in conversational agents. By adapting transformers for streaming via causal attention and triggered mechanisms, the system achieves low-latency performance while maintaining high fidelity in prosody preservation and semantic coherence. Key innovations include shared encoder layers for efficiency, hybrid decoding paths for modality-specific outputs, and joint optimization across diverse objectives like word error rate minimization and perceptual quality enhancement. Evaluations on standard benchmarks demonstrate superior results, with latency under 200ms and error rates rivalling non-streaming baselines, paving the way for deployment in voice assistants, live captioning, and real-time dialogue systems. This unified approach not only reduces model complexity but also advances end-to-end learning for dynamic audio-to-multimodal generation tasks.

Abstract: This paper formulates and analyzes a novel compartmental model to study the spatial dynamics of corruption, framed as a pathogenic social strategy within a biological resource-competition framework. The model incorporates a renewable resource, whose scarcity drives the transmission of a corrupt strategy among a population of cooperators. The population is stratified into Cooperators (S), Corruptors (C), and Immunes/Enforcers (I), interacting within and between two connected patches via migration. The model exhibits a resource-dependent transmission rate and predator-prey dynamics between Corruptors and Enforcers. We establish the well-posedness of the coupled two-patch system by proving the positivity and boundedness of solutions. The system exhibits a corruption-free equilibrium, whose local and global stability is determined by patch-specific basic reproduction numbers R₀⁽¹⁾ and R₀⁽²⁾ , as well as a system-level reproduction number R0 that incorporates migration. We derive critical migration thresholds where the stability of the corruption-free state changes. Bifurcation analysis reveals the existence of a forward transcritical bifurcation at R₀ = 1, implying that reducing the system-level reproduction number below unity is sufficient to eliminate the corrupt strategy even in connected populations. Sensitivity analysis via Partial Rank Correlation Coefficients (PRCC) identifies the most critical parameters influencing R₀⁽ⁱ⁾ , providing evidence-based policy insights. Numerical simulations corroborate our analytical findings and explore the impact of asymmetric migration on the persistence of corruption. This work provides a theoretical foundation for understanding corruption through an ecological and spatial lens, highlighting the paramount importance of resource availability, enforcement mechanisms, and cross-border connectivity.

Abstract: Rapid urbanization has aggravated the challenges in sustaining the peri-urban rice farming sector. The challenges arising from rapid urbanization are threatening rice farmers in peri-urban areas due to increasing economic and land pressures. This has caused a significant marginalization among rice farmers. In Indonesia, despite contributing 13.28% of the national GDP in 2021, the agricultural sector is dominated by marginal farmers who struggle with poverty and lack of land ownership. This study aims to identify different pathways for marginalization of rice farmers by integrating spatiotemporal land use and land cover (LULC) change analysis, landscape fragmentation metrics, and system dynamics through causal loop diagrams (CLD). Furthermore, the redefinition of the term marginal rice farmers is done by considering the total cultivated rice field and broader factors that contribute to the self-reinforcing loop of marginalization. This study shows that rice farmer marginalization in peri-urban areas is caused by small land size or poverty, and reinforcing feedback between ecosystem service degradation, productivity decline, economic pressure, and land conversion that interact differently across landscape configuration. Moreover, this study enhances the understanding of peri-urban agricultural transformation and provides landscape-sensitive policy insights to support inclusive and resilient agricultural systems by reconceptualizing marginalization of rice farmers as a dynamic socio-spatial process.

Abstract: The appearance of supermassive black holes (SMBHs) within approximately the first 800 million years after the Big Bang remains subjects of intense scrutiny and debate. In this work, we investigate the growth of black holes in a decaying vacuum. This scenario may offer novel insights into the formation and growth of SMBHs.

Abstract: Tropical forests are predicted to become carbon sources by mid-century under climate change. However, this trajectory may not be inevitable for forests under long-term protection. Using 12 years of eddy covariance flux data from a strictly protected tropical forest in Xishuangbanna, China, we develop an explainable machine learning framework (SHAP + Structural Equation Modeling) to disentangle the environmental drivers of net ecosystem exchange (NEE) and evapotranspiration (ET), and project their future trajectories under four CMIP6 climate scenarios. We find a fundamental divergence: while conventional climate models predict a sink-to-source transition by 2050–2066, our data-driven model—trained on conservation-era observations—projects a persistent carbon sink through 2100 across all scenarios. This divergence suggests that long-term protection may buffer tropical forests against climate-driven decline, challenging the prevailing narrative of inevitable carbon loss. We further identify critical environmental thresholds—solar radiation (~200 W m⁻²) and air temperature (~25°C)—beyond which carbon uptake efficiency declines. Our findings provide empirical support for nature-based climate solutions and highlight the need to integrate conservation legacies into Earth system models.

Abstract: As service robots increasingly enter public buildings such as hospitals and offices, human-robot sharing space has emerged as a pivotal topic in architectural design field, yet its relevant theoretical framework remains underdeveloped and incomplete. Existing frameworks—including Human-Robot Interaction (HRI), Human-Robot Collaboration (HRC), and Human-Robot Coexistence—have advanced research on interaction, coordination, and safety, but most regard the built environment as a passive backdrop, overlooking its active design value. This review retrieved literatures from 2000 to 2026 across four databases (Web of Science, Scopus, IEEE Xplore, and ScienceDirect) and analyzed 183 core publications using CiteSpace, systematically synthesizing the interdisciplinary knowledge in this field. The study introduces "Human-Robot Sharing Space (HRSS)" as an independent conceptual framework, repositioning the built environment from an interactive background to a core design variable while clarifying its boundaries with other traditional frameworks. Through bibliometric analysis, it reveals the field’s evolutionary trajectory from basic technical exploration to scenario-specific refinement. Finally, five systematic gaps in current research are identified: interdisciplinary theoretical integration, transferability to real-world scenarios, multidimensional evaluation indicators, coverage of architectural typology, and longitudinal empirical studies. This review bridges the gap between robotic technology and architectural design needs, providing a theoretical foundation for constructing an environment-centric, scale-inclusive, and practical design framework for HRSS.

Abstract: Consciousness presents a structural puzzle: a unified, context-sensitive, globally integrated mode of experience emerging from distributed neural dynamics. While classical neuroscience has mapped synaptic, oscillatory, and network-level mechanisms with increasing precision, debate persists as to whether classical formalisms fully capture the integrative and contextual features of conscious processing. This review examines whether quantum principles offer explanatory leverage in two distinct senses: as formal mathematical frameworks for modeling contextual cognition, and as mechanistic hypotheses proposing biologically instantiated non-classical states. We survey empirical and theoretical developments spanning zero-quantum-coherence in MRI signals, entanglement-structured learning paradigms, quantum-inspired computational models, and proposed neural substrates, including microtubules, nuclear spins, and photonic architectures. Although certain findings have been interpreted as consistent with a non-classical structure, no study to date has demonstrated entanglement, long-lived coherence, or collapse dynamics in neural tissue under operational criteria comparable to those used in controlled quantum systems. Replication remains limited, biological entanglement witnesses are not yet established, and nonlinear classical dynamics can reproduce many putative quantum signatures. Accordingly, the decisive question is not whether the brain is quantum, but whether its dynamics exceed the explanatory reach of rigorously defined classical models. Progress hinges on replication, adversarial scrutiny, and operational criteria precise enough to discriminate genuine non-classical correlations from classical complexity. Whether quantum mechanisms ultimately prove necessary or refined classical models remain sufficient, this inquiry compels a deeper understanding of integration, contextuality, and the physical constraints shaping conscious experience.

Abstract: Motivated by Matsas et al. (2024), who demonstrated that time can serve as the fundamental unit for physical quantities, thereby obviating the need for traditional length–mass–time (LMT) dimensions, this study expands on some of the presented results. Using a Lorentz transformation (LT) matrix approach, we first validate the three-clock protocol, confirming distance derivation as a function of three proper clock times in a round-trip-like arrangement in Minkowski spacetime and additionally identifying moving clocks velocities without a distance measurement, which is already implicitly identified. The investigation was then extended to Tangherlini’s 4D spacetime framework (1958) to test the hypothesis that absolute velocity can be resolved through subluminal motion experiments. While initial three-clock scenarios resulted in systematic absolute velocity cancellation, a breakthrough was achieved by applying relativistic transverse Doppler effect logic. This approach successfully circumvents cancellation effects by identifying those electromagnetic waves in transit as becoming ‚anonymous‛ and owned by the Absolute Rest Frame (ARF), independent of source origin. We demonstrate that the ratio of transverse to longitudinal wave-vector components ky/kx provides a direct measure of the peculiar velocity relative to the Cosmic Microwave Background (CMB), fully reconciling with aberration angle methodologies utilised in Planck 2013 mission measurements. The findings reveal that both frameworks are mathematically equivalent representations of the same underlying reality, inevitably predicting absolute velocity despite historical objections. Consequently, a plausible absolute velocity methodology without instantaneous signals is proven possible, closing the "cancellation gap" via wave-vector geometry, and confirming the Tangherlini and special relativity theory (STR) frameworks.

Abstract: Breast cancer remains one of the leading causes of cancer-related morbidity and mortality worldwide, with tumour recurrence and therapeutic resistance largely driven by the immunosuppressive tumour microenvironment (TME). Conventional systemic chemotherapy and immunotherapy often suffer from poor tumour selectivity, systemic toxicity, and limited immune activation within the acidic and hypoxic TME. In this context, 4D-printed pH-responsive nanofiber implants have emerged as a next-generation platform capable of delivering spatiotemporally controlled therapy tailored to dynamic tumour conditions. Unlike static 3D constructs, 4D systems incorporate stimuli-responsive polymers that undergo programmed structural or functional transformations in response to environmental triggers such as acidic pH, enabling site-specific drug release. This review critically examines the design principles of pH-responsive nanofiber implants, including polymer selection, fabrication strategies, cytokine nano-assembly, and controlled release kinetics. Special emphasis is placed on TME modulation, highlighting how localised delivery of immune-stimulatory agents such as interleukin-15 and interleukin-2 can enhance natural killer cell activation, promote artificial immune synapse formation, and induce tumour apoptosis while minimising systemic toxicity. Furthermore, we analyse the translational challenges associated with manufacturing scalability, sterilisation, regulatory classification, and long-term implant safety. By integrating smart biomaterials engineering with immunotherapeutic strategies, 4D-printed nanofiber implants represent a transformative approach for localised breast cancer treatment. However, successful clinical translation will require interdisciplinary optimisation across materials science, pharmaceutical engineering, and regulatory frameworks. This review outlines future directions toward personalised, microenvironment-responsive cancer immunotherapy platforms.

Abstract:

Urban coastal wetlands along the Peruvian Pacific coast are increasingly affected by urban expansion, pollution, and hydrological alterations, compromising their ecological integrity. In this context, the spatiotemporal variation of the aquatic macrophyte community and its relationship with limnological conditions and drivers of change were evaluated in the Santa Rosa wetland (Chancay, Lima). The objective is to evaluate the spatiotemporal variation of the aquatic macrophyte community in the Santa Rosa wetland and analyze its relationship with physicochemical limnological variables and drivers of change. Sampling was conducted during two contrasting hydrological seasons in 2022: T1 (summer) and T2 (winter), at six sampling points (P1–P6). Physicochemical variables (water depth, temperature, pH, conductivity, TDS, TSS, dissolved oxygen, turbidity, nitrate, ammonium, phosphorus, and dissolved organic matter) were measured, and the relative abundance of aquatic macrophytes was evaluated. Drivers of change were identified through direct observation and a structured matrix, with a PCoA performed to summarize spatiotemporal trends. Data were analyzed using Principal Component Analysis (PCA), Co-inertia analysis, and Multi-Response Permutation Procedures (MRPP). Significant spatiotemporal variation was observed in physicochemical parameters (p < 0.05), with moderate covariation between the two matrices (RV = 0.47). A total of ten aquatic macrophyte species were recorded, with higher abundance of Pontederia crassipes and Pistia stratiotes in T1, and Hydrocotyle ranunculoides and Bacopa monnieri in T2. The most relevant drivers of change were solid waste, livestock grazing, organic contamination, and urban expansion. Spatial heterogeneity was observed in the drivers of change affecting the Santa Rosa wetland, forming a mosaic of areas with different impact profiles. Despite multiple anthropogenic pressures, the Santa Rosa wetland maintains a limnological structure and a functionally coupled macrophyte community, evidencing ecological resilience to environmental degradation. The observed covariation between physicochemical conditions and vegetation confirms the persistence of essential ecological processes, even within an altered urban context. This study demonstrates that integrating biotic components, limnological variables, and drivers of change is fundamental to understanding and monitoring the ecological dynamics of urban wetlands along the Peruvian coast.

Abstract: Background/Objectives: Wound healing is a complex biological process involving co-ordinated interactions among inflammatory cells, growth factors, extracellular matrix components, and resident tissue cells. Despite significant advances in experimental research, translation of these findings into clinical practice remains limited, partly due to the lack of reproducible and ethically accessible human wound models. Pilonidal disease, a chronic inflammatory condition of the sacrococcygeal region, is frequently treated by surgical excision with healing by secondary intention. The resulting open wound provides a unique opportunity to study the natural progression of human tissue repair. Methods: This narrative review examines current knowledge on wound-healing phys-iology, commonly used experimental wound models, and clinical studies related to pi-lonidal disease. Evidence from experimental, translational, and clinical literature was evaluated to explore the potential of open pilonidal excision wounds as a standardized human model for wound-healing research. Results: Following open excision, healing typically occurs within 4–10 weeks through the classical phases of inflammation, pro-liferation, and tissue remodeling. During this period, the wound remains externally accessible, allowing repeated clinical observation and serial collection of tissue samples, wound fluid, and exudate. This accessibility facilitates investigation of key biological processes, including angiogenesis, fibroblast proliferation, epithelial migration, cyto-kine signaling, and extracellular matrix remodeling. Compared with in vitro systems and animal models, the open pilonidal wound offers direct insight into human wound biology under clinically relevant conditions. Conclusions: Open pilonidal excision wounds constitute a reproducible and ethically feasible in vivo human model for translational wound-healing research. This model may support biomarker discovery and contribute to the development of new therapeutic strategies for impaired healing and chronic wounds

Abstract: In this study, we developed an improved electrical impedance method for measuring oil film thickness with a correction for surface roughness effects. Statistical analysis of the oil film thickness distribution revealed that rough surfaces exhibit higher capacitance values than those predicted by the ideal parallel-plate model, despite having the same mean film thickness. Consequently, a corresponding roughness correction formula was derived. The accuracy of the method was verified in ball-on-disc type apparatus using balls with a rough surface. The corrected oil film thickness agreed more closely with the Hamrock-Dowson equation and with optical interferometry measurements than did the uncorrected result. These outcomes confirm that oil film thickness can be estimated considering surface roughness. The technique is therefore expected to facilitate the optimization of lubrication conditions and enable more reliable bearing-life prediction.

Abstract: Oral squamous cell carcinoma (OSCC) remains associated with poor outcomes and functional impairment despite multimodal treatment, underscoring the need for effective and less invasive therapeutic strategies. Ephrin type-B receptor 4 (EPHB4) is frequently expressed on OSCC cells and represents a potential target for chimeric antigen receptor (CAR)-T cell therapy. In this study, we evaluated the antitumor activity of EPHB4-targeted CAR-T cells (131CAR) using in vitro cytotoxicity assays against the OSCC cell line HSC-4 and an orthotopic tongue patient-derived xenograft (PDX) model established from an OSCC tumor (OC-15) in NOD scid gamma (NSG) mice. Flow cytometry confirmed EPHB4 expression in HSC-4 cells (~90%) and OC-15-derived tumor cells (~68%). In vitro assays demonstrated effector-to-target ratio–dependent cytotoxicity of 131CAR against HSC-4 cells. In vivo, both intratumoral and intravenous administration of 131CAR significantly reduced tumor burden compared to control groups, with greater tumor reduction observed following intratumoral delivery. Immunohistochemistry revealed infiltration of human CD8⁺ cells and decreased tumor area in treated tumors, and digital pathology analysis corroborated the reduced tumor area. These findings indicate that EPHB4-targeted CAR-T cells exert antitumor activity in an orthotopic OSCC model and suggest that local administration may enhance therapeutic efficacy in anatomically relevant settings.

Abstract: This paper examines the strategic manipulationof government debt data—referred to as ”fiscal rule stretch-ing”—among European Union member states, particularly inresponse to electoral incentives, financial market stress, and theconstraints of the Stability and Growth Pact (SGP). Using Euro-stat revisions of government debt figures, we find that countrieswith higher debt levels, particularly those exceeding the 60% ofGDP SGP threshold, are more likely to have their debt figuresrevised upwards. Our analysis further reveals that eurozonemembers are more inclined to engage in rule stretching, especiallywhen facing Excessive Deficit Procedure (EDP) enforcement.Election timing plays a crucial role in this behavior, with un-scheduled elections and proximity to elections both significantlyincreasing the likelihood of debt revisions. Financial market stressamplifies these effects, as governments under pressure may resortto optimistic accounting to present more favorable debt statistics.While fiscal transparency appears to be positively associated withdebt revisions, suggesting that transparent governments may relyon more sophisticated methods of rule stretching, GDP growthdoes not show a significant impact. Overall, our findings highlightthe intersection of domestic political cycles, financial constraints,and EU-level oversight in shaping fiscal reporting practices acrossmember states.

Abstract: Artisanal and illegal gold extraction in ecologically sensitive tropical landscapes can generate persistent environmental damage and public fiscal liabilities that accumulate even under formal mining prohibitions. A decision-grade pipeline is presented that converts observable environmental signals into (i) spatial prioritization surfaces, (ii) phase-timed remediation portfolios, and (iii) present-value (PV) comparisons of legislative policy pathways under uncertainty, demonstrated for the Crucitas mining landscape (Cutris, northern Costa Rica). Five linked models are implemented. Remote-sensing change proxies are derived using consistent baseline (January 2019–December 2020) and recent (February 2024–January 2025) windows; multi-criteria indices then produce a 0–100 grid-cell prioritization surface integrating land, water, and hydrologic dimensions. This prioritization output is translated into a phased remediation portfolio across 1,324 costed grid cells, yielding a gross liability of US$548.0 million (10-year PV; 5% discount rate). PSA-related credits total US$167.3 million PV; enforcing a cell-level non-negativity floor yields a baseline net PV of US$408.0 million (simple gross-minus-credits would be US$380.8 million). Deterministic policy overlays produce policy-adjusted net PV of US$336.1 million under Exp. 24.717 (minimum 5% royalty case; Δ = −US$71.9 million vs baseline; modeled royalty PV = US$93.8 million), US$503.0 million under Exp. 24.675 (Δ = +US$95.0 million), and US$510.3 million under Law No. 8904 (Δ = +US$102.3 million). Royalty-rate sensitivity cases for Exp. 24.717 yield deterministic policy-adjusted net PV of US$242.3 million (10%) and US$148.5 million (15%). Monte Carlo propagation yields a right-tailed baseline distribution (P10–P90 = US$385.4–519.1 million; P50 = US$450.1 million), with exceedance probabilities P(>US$400 million) = 0.8357 and P(>US$500 million) = 0.1786. Policy-adjusted uncertainty bounds indicate substantially reduced exceedance risk under Exp. 24.717 (5% royalty case; P(>US$400 million) = 0.3542; P(>US$500 million) = 0.0153), with further reductions at higher take-rates (10%: P(>US$400 million) = 0.0375; P(>US$500 million) = 0.0007; 15%: P(>US$400 million) = 0.0028; P(>US$500 million) = 0.0000), while non-mining pathways shift the distribution upward. The results support PV-consistent, uncertainty-aware ranking of contested pathways, with outcomes conditional on enforceable offsets, credible enforcement effectiveness, and residual-risk provisioning. The framework is transferable to other contested mining landscapes where phased interventions and policy alternatives require fiscally comparable evaluation.

Abstract:

Methane (CH₄) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH₄ emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions, however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH₄ emissions at the facility-level. Near-field quantification of methane (CH₄) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian Plume (GP) and backward Lagrangian Stochastic (bLS), by comparing calculated CH₄ emissions to controlled single-point emissions of between 0.4 and 5.2 kg CH₄ h^-1. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. Comparison shows the bLS approach showed better predictive performance with twice as many emission estimates were within a factor of two (FAC2) of the known emission rates compared to those calculated using the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows the lateral and vertical alignment of source and sensor plays a critical role in emission estimations as measurements made closer to the plume centerline and at a distance between 40 to 80 m downwind yielded the best FAC2 agreement. High wind meander degraded ability of both approaches to generate representative emissions particularly with the GP approach as it violates the modelling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While it is likely that the results presented here are suitable for informing leak detection technology in relatively flat unvegetated environments, it is currently unknown if these findings will be applicable in more vertiginous or heavily vegetated oil and gas producing regions of the Marcellus or Uinta Basins.