Abstract: Vehicular Ad-hoc Networks (VANETs) face critical challenges regarding intermittent connectivity and latency due to high node mobility, often resulting in a performance trade-off between reactive and proactive routing paradigms. This study aims to resolve these inherent limitations and ensure reliable communication in volatile environments. We propose a novel context-aware framework, the Dynamic Hybrid Routing Protocol (DHRP), which integrates Ad hoc On-Demand Distance Vector (AODV) and Optimized Link State Routing (OLSR). Distinguished by a predictive multi-criteria switching logic and a hysteresis-based stability mechanism, the proposed method employs a synergistic cross-layer framework that adapts transmission power and routing strategy in real time. Validated through extensive simulations using NS-3 and SUMO, experimental results demonstrate that the protocol outperforms traditional baselines and contemporary benchmarks across all key metrics. Specifically, the system maintains a Packet Delivery Ratio (PDR) exceeding 90%, ensures end-to-end delays remain under the safety-critical 40 ms threshold, and achieves energy savings of up to 60%. In conclusion, DHRP successfully resolves the routing performance dichotomy, providing a scalable, energy-efficient foundation for next-generation Intelligent Transportation Systems (ITS) in which reliable safety messaging is paramount.

Abstract: The field of agentic artificial intelligence is transitioning from reasoning-centric architectures toward systems explicitly designed for reliability under uncertainty. Current agent frameworks, while effective in controlled environments, exhibit cognitive rigidity—an inability to proactively correct planning trajectories when confronted with unexpected faults. This paper introduces Adapt-Plan, a foundational hybrid architecture that reformulates planning as a control-theoretic process by elevating the Planning Efficiency Index (PEI) from a post-hoc evaluation metric to a real-time control signal. Through dual-mode planning (strategic and tactical) and Extended Dynamic Memory (EDM) for selective experience consolidation, Adapt-Plan enables agents to detect behavioral drift early and initiate corrective adaptation before catastrophic degradation occurs. Controlled validation across 150 episodes demonstrates PEI=0.91 ± 0.06 and FRR=78% ± 4.2% (95% CI [74%, 82%], p < 0.001, Cohen’s d = 2.18 vs. ReAct), establishing the algorithmic viability of metric-driven adaptation. Comprehensive ablation studies isolate component contributions, revealing that PEI-guided control accounts for 31% of performance gains. These architectural principles were subsequently validated at scale through rigorous certification frameworks, confirming that PEI-driven control generalizes to deployment-grade reliability when augmented with safety protocols. This work establishes the conceptual foundation for reliable agentic AI through the tight integration of architecture, metrics, and control.

Abstract: Background: Cardiac rehabilitation (CR) is a key component of secondary prevention after acute coronary events, coronary and valve interventions, and device implantation, yet participation and longterm adherence remain suboptimal. Digital technologies offer the potential to extend CR beyond the centrebased model and to support more flexible, patientcentred care. Methods: This narrative review synthesizes original clinical studies published between 2005 and 2025 that evaluated the use of digital technologies as an integral part of CR in adults after myocardial infarction, revascularization, valve procedures or implantation of cardiac devices. Interventions were grouped into four categories: mobile health (mHealth) and telerehabilitation, virtual reality (VR) and exergaming, virtual education platforms, and other multicomponent digital CR solutions. Only original studies with clinical, functional, or patientreported outcomes were included. Results: Twenty-one studies on the categories mentioned above met the eligibility criteria. mHealthenabled homebased or hybrid CR programs consistently achieved improvements in functional capacity and physical activity that were broadly comparable to centrebased CR, with generally high adherence. VR and exergaming interventions were feasible and safe, produced at least similar functional gains, and showed more consistent benefits as far as anxiety levels and engagement levels. Virtual education platforms delivered knowledge and produced behaviour change similar to traditional education and, in some studies, supported better control of blood pressure and lipids. Comprehensive digital CR platforms improved riskfactor profiles and quality of life to a degree comparable with facetoface CR. Conclusions: Digital technologies can credibly support core objectives of CR in highrisk patients and expand access, but must be implemented as a complement to, rather than a replacement for, multidisciplinary, patientcentred rehabilitation.

Abstract:

Background: Accurate prognostic assessment remains crucial in metastatic renal cell carcinoma (mRCC), especially as treatment options have expanded beyond vascular endothelial growth factor (VEGF)–targeted therapies to include immune checkpoint inhibitors (ICIs) and ICI–TKI combinations. The widely used IMDC classification shows important limitations in the modern therapeutic era, highlighting the need for complementary prognostic tools. In this context, the Meet-URO and CANLPH scores—incorporating clinical, inflammatory, and nutritional markers have emerged as promising alternatives. Objective: To evaluate and compare the prognostic performance of the Meet-URO and CANLPH scoring systems in a real-world mRCC cohort predominantly treated with first-line tyrosine kinase inhibitor (TKI) monotherapy due to limited access to ICI-based combinations. Methods: This retrospective single-center study included 112 patients with mRCC. The Meet-URO score was calculated for all patients, while the CANLPH score was assessed in 56 patients with complete laboratory data. CAR, NLR, and PHR were computed using baseline pre-treatment measurements. Overall survival (OS) and progression-free survival (PFS) the latter defined exclusively for first-line therapy—were estimated using the Kaplan–Meier method. Correlations between inflammatory markers and survival outcomes were analyzed using Spearman’s rho. Results: Meet-URO demonstrated clear prognostic stratification across all five categories, with the most favorable outcomes in score group 2 and progressively poorer OS and PFS in higher-risk groups. CANLPH also showed meaningful survival discrimination, with the highest inflammatory group (score 3) exhibiting markedly reduced OS and PFS. CAR was the strongest individual predictor of survival, while NLR and PHR showed weaker associations. Conclusion: Both Meet-URO and CANLPH provide strong, complementary prognostic information in mRCC, even in a cohort largely treated with TKI monotherapy. Their integration into routine risk assessment may enhance clinical decision-making, particularly in resource-limited settings.

Abstract: L-asparaginase (L-Aspase) enzyme has found applications in medicine for treatment of various cancers. Herein, we report single-molecule study of thermal denaturation of L-Aspase within 25°C to 60°C temperature range by atomic force microscopy (AFM) and by single-molecule sensing with a (solid state nanopore)-based electrical detector (SSNPED). AFM has allowed us to reveal a thermally induced changes in aggregation state of L-Aspase and in its adsorbability on mica. At the same time, the configuration of the enzyme’s globule spatial conformation has been found to alter according to data obtained with the SSNPED. Our results reported open up opportunities for further development of anti-cancer drugs.

Abstract: Background/Objectives: Nutritional risk screening is critical in the management of gynaecologic oncology (GO) surgical patients. Malnutrition is prevalent in this population and is associated with poorer surgical outcomes, including increased morbidity, prolonged hospital stays, and reduced survival rates. Nevertheless, the optimal nutritional screening tools for this patient group remain undefined. Methods: We conducted a narrative review to critically appraise commonly used nutritional screening and assessment tools in surgical GO patients. To highlight practical challenges in accurately identifying at-risk individuals, we incorporated findings from our recent clinical audit. Results: Several nutritional screening and assessment tools were identified. The results varied considerably between tools. The presence of ascites and rapid deterioration in oral intake were frequently overlooked, leading to under-recognition of malnutrition. These issues were corroborated by our audit findings. Emerging strategies including determining body composition from routine preoperative CT scans show promise. Conclusions: Accurate nutritional assessment is imperative to improve surgical outcomes in surgical GO patients. As currently no gold standard currently exists for this population, bespoke approaches to address disease-specific nutritional considerations are urgently needed to identify those at risk and allow for timely nutritional interventions. Integrating CT-based body composition analysis can provide an objective solution, thus requiring further investigation.

Abstract:

Background/Objectives: IgA nephropathy (IgAN) is the most common primary glomerulopathy worldwide; it is characterized by a complex pathophysiology involving several inflammatory pathways. The Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway may be critical in this process. This study aimed to investigate the role of this pathway in IgAN and examine related tissue inflammatory markers. Methods: We analyzed 63 biopsy-confirmed patients with IgAN and performed immunohistochemical analysis on renal samples. A panel of antibodies targeting the JAK/STAT pathway, including JAK2, JAK3, p-STAT, STAT3, and MAPK/ERK, was used for this analysis. Six kidney tumor border samples were used as controls. Additionally, CD68 staining was used to evaluate tissue inflammation in the kidney biopsies. Results: Patients with IgAN showed a significantly higher cellular density of JAK3 staining at the glomerular level compared to controls, indicating JAK3 activation (p < 0.0002). Nevertheless, the correlation between JAK3 positivity in glomeruli and clinical parameters such as the initial and final estimated glomerular filtration rate (eGFR) and proteinuria was not statistically significant. Identical results were obtained with CD68+ macrophage counts in the glomerular compartment, which did not show any correlation with clinical parameters, while CD68+ tubulointerstitial staining demonstrated a significant correlation with both initial (p = 0.002) and final eGFRs (p = 0.0014), proteinuria (p = 0.010), and interstitial fibrosis (p < 0.001), as well as with renal disease progression (p = 0.005). Conclusions: Patients with IgAN exhibited activation of the JAK/STAT pathway, in contrast to controls. Macrophage CD68 staining in the tubulointerstitial area increased and was associated with clinical and laboratory parameters such as eGFR and proteinuria. Additionally, MEST-C histological parameters, such as segmental glomerulosclerosis (S0/S1), tubular atrophy/interstitial fibrosis (T0/T1/T2), and crescents (C0/C1/C2), were associated with a higher number of CD68+ cells.

Abstract:

Accurately estimating actual evapotranspiration (ETa) is essential for sustainable water management, particularly in semi-arid regions. Although the SAFER algorithm provides a practical remote sensing-based approach, its sensitivity to parameter settings and reduced performance during dry periods limit its reliability. This study develops four parametric ETa models—two linear (LM-I, LM-II) and two nonlinear (NLM-I, NLM-II)—and recalibrates SAFER coefficients via a simulation/optimization (S/O) approach. Models were evaluated using Landsat-8 data (LST, NDVI, αₛ) and reference evapotranspiration (ETo), and compared with machine learning methods: Random Forest (RF), Bagged Trees (BT), Support Vector Machines (SVM), and Generalized Additive Models (GAM). Results indicate that nonlinear models better capture the physical behavior of ET processes and outperform linear models across key metrics. In particular, the NLM-II model achieved R² = 0.8295 and RMSE = 0.4913 on the test set, surpassing SAFER (R² = 0.8195, RMSE ≈ 0.5713), LM-II, and the best soft computing model, BT (R² = 0.8137, RMSE = 0.5084). Its physically grounded structure ensures stable, interpretable predictions that accurately reflect water–energy interactions and seasonal dynamics. These findings demonstrate that compact, physically based nonlinear parametric models provide a robust, operationally practical solution for ETa estimation under sparse Landsat-based datasets, outperforming both linear and black-box machine learning approaches.

Abstract: Estimating surface soil moisture in semi-arid regions is challenging due to its high spatial and temporal variability, the scarcity of in-situ measurements, and the limitations of optical sensors in the presence of cloud cover and vegetation cover. Synthetic Aperture Radar (SAR) sensors, such as Sentinel-1, overcome these constraints by operating in the microwave domain and providing high-resolution data regardless of atmospheric conditions or daylight availability. This enables the application of inverse semi-empirical models, notably the Hallikainen model for the soil dielectric constant and the Dubois model for backscattering. This study proposes an integrated methodology applied to the municipality of Villaconejos (Madrid, Spain) over the period 2015–2025. The approach was initially calibrated on a pilot plot near Balcón del Tajo using field measurements of soil moisture and soil texture data (sand and clay content) obtained from the SoilGrids platform. Starting from Sentinel-1 VV and VH backscatter coefficients, the combined Hallikainen–Dubois model is inverted through an iterative search over a range of volumetric soil moisture values (0.02–0.45 m* m*) and surface roughness values (0.85–2 cm), selecting the parameter pair that minimises the difference between modelled and observed backscatter. The calibrated methodology is then extrapolated across the entire municipality of Villaconejos using Empirical Bayesian Kriging Regression Prediction (EBK-RP), incorporating topographic covariates (digital elevation model, slope, aspect), hydrological covariates (Topographic Wetness Index, TWI), and vegetation covariates (NDVI). The results include annual and seasonal maps of near-surface volumetric soil moisture (0–5 cm depth) at 10 m resolution and, after a geostatistical downscaling procedure, at 2 m resolution. Additional outputs comprise analyses of temporal variations between wet and dry periods and spatial patterns related to land use and topography. The developed methodology provides continuous, high-resolution, operational, and low-cost soil moisture estimates, representing a valuable tool for water resource management and agro-environmental monitoring in semi-arid regions.

Abstract:

The integration of artificial intelligence (AI) in precision agriculture marks a transformative step toward sustainable, efficient, and data-driven farming practices. By merging AI with predictive analytics and autonomous monitoring systems, agriculture is empowered to achieve higher crop yields and maintain robust soil health. AI-driven models process vast datasets from sensors, drones, and IoT devices to predict crop performance, recommend targeted interventions, and enable real-time monitoring of field conditions. This synergy not only allows for early detection of threats such as pests or nutrient deficiencies but also ensures optimized resource utilization, reducing environmental impact. The adoption of these intelligent systems paves the way for a resilient agricultural landscape that can adapt to the challenges posed by climate variability and the growing global food demand, ultimately fostering productivity and long-term ecological sustainability.

Abstract: Bioarchaeological materials represent finite and irreplaceable resources, with many analytical techniques requiring consumptive sampling that permanently limits future research opportunities. This challenge is particularly acute for Modern era contexts (1492–1945 CE), where industrial and colonial period collections offer crucial evidence of health transitions and disease emergence yet remain under-utilised due to data accessibility challenges. Evidence from 145 bioarchaeology specialists across 23 countries demonstrates that whilst 97% recognise data reuse as critical, fewer than half consistently implement basic measures such as persistent identifiers. Only ancient DNA research consistently meets FAIR (Findable, Accessible, Interoperable, Reusable) standards. Meanwhile, data volumes expand exponentially through technological advances. The situation is unsustainable and ethically problematic. This perspective argues three integrated commitments are essential: universal adoption of FAIR principles with appropriate infrastructure, implementation of CARE (Collective benefit, Authority, Responsibility, Ethics) principles ensuring ethical treatment of human remains, and strategic development of artificial intelligence and machine learning tools for knowledge extraction. The finite nature of bioarchaeological materials makes transformation urgent. Every sample destroyed without proper data preservation represents irreversible loss of knowledge about human heritage.

Abstract: Staphylococcus cohnii, a coagulase-negative staphylococcus (CoNS), is a human skin microbiome constituent with significant antibacterial potential due to its production of bacteriocins. This study aims to characterize the genome of S. cohnii 148-XN2B 18.2 isolated from healthy human skin, focusing on its bacteriocin genes, phylogenetic relationships, and potential antimicrobial applications. Whole-genome sequencing (WGS) was conducted by integration of Illumina and Oxford Nanopore platforms. Bacteriocin genes were identified using BLASTp against reference databases, while phylogenetic analysis was conducted using MAFFT and IQ-TREE. Comparative genomic analysis of bacteriocin clusters was performed using Clinker. The genome of S. cohnii 18.2 consists of a circular chromosome of 2,768,657 bp with a GC content of 32.7%. Functional annotation revealed 5 putative bacteriocin genes. Phylogenetic analysis confirmed a close evolutionary relationship with S. arlettae. Comparative genomics showed high conservation of bacteriocin clusters including Colicin V-like, Enterocin B-like, Pyocin AP41-like and Bacteriocin 28b-like between S. cohnii and S. arlettae.

Abstract:

This paper presents a detailed re-examination of the conformal compactification of Minkowski space, \( \overline{M} \), constructed as the projective null cone of the six-dimensional space \( \mathbb{R}^{4,2} \). We provide an explicit and basis-independent formulation, emphasizing geometric clarity. A central result is the explicit identification of \( \overline{M} \) with the unitary group U(2) via a diffeomorphism, offering a clear matrix representation for points in the compactified space. We then systematically construct and analyze the action of the full conformal group \( \mathrm{O}(4,2) \) and its connected component \( \mathrm{SO}_0(4,2) \) on this manifold. A key contribution is the detailed study of the double cover, \( \overline{\overline{M}} \), which is shown to be diffeomorphic to \( S^3 \times S^1 \). This construction resolves the non-effectiveness of the \( \mathrm{SO}(4,2) \) action on \( \overline{M} \), yielding an effective group action on the covering space. A significant portion of our analysis is devoted to a precise and novel geometric characterization of the conformal infinity. Moving beyond the often-misrepresented ``double cone'' description, we demonstrate that the infinity of the double cover, \( \overline{\overline{M}}_\infty \), is a squeezed torus (specifically, a horn cyclide), while the simple infinity, \( \overline{M}_\infty \), is a needle cyclide. We provide explicit parametrizations and graphical representations of these structures. Finally, we explore the embedding of five-dimensional constant-curvature spaces, whose boundary is the compactified Minkowski space, and discuss the interpretation of geodesics within these domains. The paper aims to clarify long-standing misconceptions in the literature and provides a robust, coordinate-free geometric foundation for conformal compactification, with potential implications for cosmology and conformal field theory.

Abstract: To overcome the low accuracy of conventional methods for estimating liquid volume and food nutrient content in bowl-type tableware, as well as the tool dependence and time-consuming nature of manual measurements, this study proposes an integrated approach that combines geometric reconstruction with deep learning–based segmentation. After a one-time camera cali-bration, only a frontal and a top-down image of a bowl are required. The pipeline automatically extracts key geometric information, including rim diameter, base diameter, bowl height, and the inner-wall profile, to complete geometric modeling and capacity computation. The estimated parameters are stored in a reusable bowl database, enabling repeated predictions of liquid vol-ume and food nutrient content at different fill heights. We further propose Bowl Thick Net to predict bowl wall thickness with millimeter-level accuracy. In addition, we developed a Geome-try-aware Feature Pyramid Network (GFPN) module and integrated it into an improved Mask R-CNN framework to enable precise segmentation of bowl contours. By integrating the contour mask with the predicted bowl wall thickness, precise geometric parameters for capacity estima-tion can be obtained. Liquid volume is then predicted using the geometric relationship of the liq-uid or food surface, while food nutrient content is estimated by coupling predicted food weight with a nutritional composition database. Experiments demonstrate an arithmetic mean error of −3.03% for bowl capacity estimation, a mean liquid-volume prediction error of 9.24%, and a mean nutrient-content (by weight) prediction error of 11.49% across eight food categories.

Abstract: India, a major emerging economy has historically been deeply affected by global economic shocks. Understanding how its key economic factors such as Index of industrial production, wholesale prices, exchange rates and oil prices respond to these events is crucial for the nation's stability. This research aims to analyse India's macroeconomic responses to these three significant global shocks such as Financial crises 2008, recurring oil price shocks and Covid-19 pandemic. Using monthly data from 1993 to 2024, this study employs co-integration tests for long-term linkages and a VECM for short-term dynamics (including IRF and FEVD). Quantile regression uncovers asymmetric crisis effects, whereas ARCH–GARCH models are employed to assess volatility persistence. The findings show long-term equilibrium linkages with significant error-correction. Further, oil price shocks affect inflation and industrial output through exchange rate adjustments. Quantile regression reveals intensified asymmetric effects at distribution extremes whereas Volatility analysis confirms clustering, with structural breaks identified during the Global Financial Crisis and COVID-19. It can be concluded that India's macroeconomic system is externally vulnerable but demonstrates partial resilience. Policy recommendations from this study includes building strategic oil reserves, adopting currency-oil hedging and enhancing overall crisis preparedness so as to achieve macroeconomic stability.

Abstract: Background/ Objectives: Pediatric palliative care aims to relieve suffering and improve the quality of life of children with serious conditions and their families. The aim of the study was to assess changes in quality of life following enrollment in a pediatric palliative care program at a tertiary care center in Mexico and to identify factors influencing these changes. Methods: This prospective cohort study included children with life-limiting or disabling conditions receiving care at a tertiary hospital in Mexico City. Quality of life was measured using the Pediatric Quality of Life Inventory Cancer Module for oncological patients and the Family Impact Module for all families at baseline, three months, and six months. Results: A total of 186 children were enrolled. While most had cancer, other diagnoses included neurological disorders and congenital anomalies. Mean child’s Quality of life scores improved from 58.9 to 77.9 (p < 0.001), and family scores from 60.1 to 78.8 (p < 0.001) over six months, with significant gains across emotional, physical, and relational domains. Families residing outside Mexico City had lower baseline scores but showed greater improvements. Families of children with non-cancer conditions experienced smaller gains. Median survival varied, with longer survival observed in children with neurological or intracranial conditions. Conclusions: Specialist pediatric palliative care improved the well-being of children and families in a middle-income setting. Equitable access should be ensured for families affected by chronic conditions, particularly those living beyond major urban areas.

Abstract:

Background/Objectives: Prosocial behaviors such as helping, sharing, and comforting constitute a core aspect of human sociality and emerge early in development. Understanding how early empathic responses are organized is central to current debates on the developmental foundations of prosociality, particularly beyond Western, educated, industrialized, rich, and democratic (WEIRD) populations. This study examined the developmental organization of early empathic responses and the contributions of age, sex, and socioeconomic context to variability in early prosocial behavior. Methods: Thirty-six Colombian children aged 14 to 30 months from three socioeconomic contexts (very low, low, and middle–high), including children from indigenous Wayuu communities, were observed during a simulated distress situation derived from the Échelle de Communication Sociale Précoce (ECSP). Empathic responses were coded using the expanded hierarchical classification proposed by Molina and Bulgarelli and summarized through an ordinal empathy score reflecting the highest level of empathic organization observed. Quantitative analyses were complemented by qualitative observations of interactional behavior. Results: Empathic response organization increased with age and was positively associated with overall socio-communicative development. No significant differences were observed according to sex or socioeconomic context. Qualitative analyses revealed a progressive organization of empathic responses, ranging from attention and discomfort to coordinated gestural and symbolic prosocial behaviors, consistent across sociocultural settings. Conclusions: Early empathy appears as an interactionally organized and developmentally robust foundation of prosocial behavior during the first three years of life. These findings contribute to ongoing discussions on the early bases of human prosociality and its expression across diverse sociocultural contexts.

Abstract: This paper examines the short-run transmission of monetary policy shocks to bank credit granted to the non-financial corporate sector in Morocco, a bank-based emerging economy. While conventional monetary theory emphasizes the interest rate channel, growing empirical evidence suggests that monetary transmission is increasingly conditioned by banks’ balance-sheet constraints and credit risk considerations. The central question addressed is whether policy-rate shocks translate into short-run credit expansion or are instead absorbed through alternative banking adjustment mechanisms. The empirical analysis relies on monthly macro-financial data over the period 2014–2024 and employs a reduced-form Vector Autoregressive (VAR) framework. Impulse response functions, forecast error variance decompositions, and Granger causality tests are used to assess the dy-namic interactions between the policy rate, non-financial corporate credit, banks’ sovereign asset holdings, and credit risk conditions.The results show that monetary policy shocks generate weak, short-lived, and economically negligible responses in non-financial corporate credit, with no evidence of sustained credit expansion following policy-rate changes. By contrast, monetary impulses are associated with systematic balance-sheet reallocation toward sovereign assets and with more pronounced, though transitory, movements in credit risk indicators. Variance decompositions further reveal that short-run credit dynamics are overwhelmingly driven by internal banking and risk-related factors, while monetary policy shocks explain only a marginal share of credit fluctuations. Overall, the findings indicate that short-run monetary transmission in Morocco operates predominantly through risk-sensitive balance-sheet adjustments rather than through direct quantity-based credit responses, thereby reframing the interpretation of weak credit reactions to monetary policy in bank-based emerging economies.

Abstract: Parent-child interaction is a foundational component of language development. This study examined parent-child interaction in deaf and hard-of-hearing children 6 or 9 months after they received hearing aids or cochlear implants. Expressive, receptive, and overall language skill were probed 9 to 18 months later. Thirteen DHH children and their parents participated in a videorecorded, semi-structured play interaction. Items from an adapted version of the Joint Engagement Rating Inventory were used evaluate parent-child interactions (i.e., fluency and con-nectedness, shared routines and rituals, child joint engagement, and parental sensitivity). Language skills were assessed using the Preschool Language Scales-5th (Zimmerman et al., 2011). Results indicate statistically significant re-lationships between child-parent joint engagement and expressive (p = .004), receptive language (p = .043), and total language scores (p = .007). The shared routines and rituals item was significantly related to expressive language (p = .037) and approached statistical significance with total language (p = .076) but was not significantly related to receptive language. The fluency and connectedness item was significantly related expressive language (p = .008) and total language (p = .028) but did not reach statistical significance with receptive language (p = .077). A quantitative measure of parental language input (i.e., words per minute) was not significantly related to any language variables.

Abstract:

We study a purely local inverse problem for non-reversible Randers metrics \( F = \|\cdot\|_g + \beta \) defined on smooth oriented surfaces. Using only the lengths of sufficiently small closed curves around a point \( p \), we prove that the exterior derivative \( d\beta(p) \) can be uniquely and stably recovered. Moreover, we establish that \( d\beta(p) \) is the only second-order local invariant retrievable from such local length measurements. Our approach is entirely metric-based, independent of geodesic flows or boundary data, and naturally extends to general curved surfaces.