Abstract: Biometric identification has become a key element in modern security and surveillance applications; however, traditional systems based on a single biometric trait often suffer from noise, distortions, and vulnerability to manipulation, which limits their reliability in real-world environments. To overcome these challenges, this study proposes a multi-pattern biometric identification model that integrates facial features and hand gesture information extracted from video data for remote identity verification. The proposed system captures real-time video of an individual approaching a sensor, selects relevant frames, and applies advanced feature extraction techniques to both facial and hand modalities, which are then fused during the evaluation stage. Identity classification is performed using a time-delay neural network (TDNN), and the model is evaluated on diverse multimedia datasets containing static facial images and dynamic hand gestures, including American Sign Language samples. Experimental results demonstrate that the multimodal approach significantly outperforms single-modal systems, achieving an accuracy of 0.98, recall of 0.98, and F1 score of 0.97, compared to lower performance when using facial or hand features independently. These findings indicate that combining multiple biometric traits enhances robustness, reduces ambiguity, and improves recognition accuracy, making the proposed approach suitable for practical biometric verification scenarios under varying environmental conditions.

Abstract: Background: Access to prosthetic, orthotic and related assistive services remains uneven globally; this manuscript examines the systemic causes and rehabilitation consequences within the context of India. We frame service gaps as health-systems failures with measurable workforce, supply-chain, financing and data components. Methods: A narrative policy review was undertaken using targeted searches of peer-reviewed literature, government reports, professional body publications, and NGO datasets. Key themes were synthesized across governance, workforce, supply chain, financing, and monitoring domains to derive pragmatic policy interventions. (Authors should replace or update search dates and data sources as required prior to submission.) Findings/Observations: Four structural deficits drive undercoverage: (1) insufficient trained P&O workforce and uneven geographic distribution; (2) fragmented manufacturing and procurement with limited quality control; (3) inadequate public financing and poor insurance/benefits coverage for device services; and (4) absence of routine service and outcome surveillance. These deficits produce preventable functional dependency, increased caregiver burden, and inequitable access—most pronounced among rural, low-income, and disabled populations. Conclusions: Closing P&O service gaps requires integrated health-systems actions: workforce scale-up and credentialing, pooled procurement and quality standards, explicit public financing pathways, and routine service/outcome monitoring. Policy recommendations (summary): Five priority actions are proposed: national workforce strategy, accreditation and CE frameworks; standardized device procurement and quality assurance; finance and benefit design for assistive services; decentralized service hubs with tele-rehabilitation links; and a national monitoring dashboard tied to performance indicators.

Abstract:

Strawberry (Fragaria × ananassa Duch.) production faces growing pressure to reduce reliance on peat and coconut coir substrates, driven by documented life cycle liabilities including carbon losses from peat extraction and embodied transport emissions from coir. Nutrient film technique (NFT), a substrate-free recirculating hydroponic system, eliminates growing media entirely and reduces material inputs across successive crop cycles, making it an environmentally attractive candidate for controlled environment strawberry production. Despite early commercial adoption in Europe during the 1970s, NFT was largely abandoned for strawberry production by the 1980s following systematic failures whose physiological basis remains incompletely characterized. This review synthesizes evidence from hydroponic systems engineering, plant physiology, and oomycete pathology to examine the two structural constraints underlying NFT’s historical rejection: dissolved oxygen depletion dynamics within recirculating nutrient solution, and exceptional susceptibility to Pythium spp. root rot. We demonstrate that these constraints are coupled rather than independent, sharing a common pathway through root-zone oxygen status. Progressive root mat development over a six-month fruiting cycle degrades passive film aeration and creates hypoxic conditions that impair root membrane integrity, alter rhizosphere exudate profiles, and facilitate Pythium zoospore encystment and necrotrophic transition. This interaction is compounded by strawberry’s exceptional oxygen sensitivity and absence of adaptive aerenchyma formation, rendering thresholds established for tomato and cucumber inapplicable to this species. We identify two prerequisite research gaps that must be resolved before NFT can be rationally reconsidered for commercial strawberry production: characterization of root mat effects on channel hydraulic performance, and establishment of a strawberry-specific dissolved oxygen threshold under NFT-relevant conditions.

Abstract: The two spot cotton leafhopper (TSCL), Amrasca biguttula (Hemiptera: Cicadellidae), is an emerging invasive pest in the southeastern United States. Although TSCL is historically associated with cotton and vegetable crops, recent detections on ornamental hibiscus have raised regulatory concern, including “Stop Sale and Hold” orders and an emergency quarantine in Texas. Despite increasing pressure on hibiscus, no insecticide efficacy data exist for ornamental systems. We evaluated the acute (0–24 h) and residual (24–96 h) toxicity of bifenthrin, flupyradifurone, and tolfenpyrad against adult and immature TSCL using a sequential cohort leaf disc bioassay. New insects were introduced at 24 h and 72 h to isolate residue based mortality from prolonged exposure effects. Bifenthrin caused the highest acute mortality at 24 h, whereas flupyradifurone and tolfenpyrad exhibited slower initial activity but strong residual performance. Immatures were more susceptible than adults across doses. By 72 h, all three insecticides produced near complete mortality, with significant treatment and dose effects confirmed by ANOVA and binomial GLM analyses. Dose–response curves showed steep concentration dependent mortality for bifenthrin and tolfenpyrad and a time dependent response for flupyradifurone. These results provide the first insecticide efficacy data for TSCL on ornamental hibiscus and offer immediate guidance for nursery producers and regulatory agencies. The findings establish a foundation for whole plant and greenhouse evaluations to support integrated management and interstate plant movement compliance.

Abstract: The Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) missions provide terrestrial water storage anomalies (TWSA) at coarse spatial resolution (300 km), limiting their application in medium-sized basins. This study develops a machine-learning framework to enhance the spatial interpretability of GRACE mascon TWSA within the 48,000 km² Hadejia-Jama’are River Basin, Nigeria. Hydroclimatic predictors derived from TerraClimate, Global Land Data Assimilation System (GLDAS), and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) were integrated within a unified 4 km spatial framework. Four machine learning models were evaluated, including Random Forest (RF), Gradient Boosting, Histogram Gradient Boosting, and a Multi-Layer Perceptron. The RF model achieved the highest skill in reproducing mascon-scale TWSA (R² = 0.937; NSE = 0.937; RMSE = 4.36 cm). Aggregation of the 4 km fields back to the mascon scale preserved basin-integrated mass (R² = 0.94), confirming consistency with the original GRACE signal. The resulting groundwater storage anomaly (GWSA) fields resolve sub-basin spatial gradients and seasonal recharge-depletion cycles that are not discernible in the native product. Validation against 31 monitoring wells yielded moderate temporal agreement (Pearson correlation coefficient, r = 0.656), with magnitude discrepancies attributable primarily to scale mismatch and hydrogeological heterogeneity. While not a substitute for in-situ monitoring, the downscaled product enhances basin-scale groundwater assessment in data-scarce semi-arid regions. The framework is transferable to comparable basins and supports regional drought monitoring and water-resource management.

Abstract: This paper develops a hybrid quantum–classical framework for adaptive AI agents, combin- ing a self-reference-aware quantum evaluation layer with a classical candidate-generation and evolutionary optimization layer. On the quantum side, we introduce a nonlinear, memory- dependent extension of open-system dynamics through St[ρ] and derive key structural proper- ties, including trace preservation, Hermiticity, pointer-basis fixed-point behavior, and practical positivity conditions in bounded-coupling regimes. On the AI-systems side, we define measur- able response metrics (χ2, ζ), introduce a compositional synergy integral Sint, and specify an online-selection plus offline-evolution pipeline. Candidate-dependent evaluation is implemented through semantic embedding and amplitude encoding, so quantum initialization reflects linguis- tic proximity rather than hash collisions. The contribution is framed as a testable theoretical architecture rather than a universal performance claim: χ2 and ζ are structural diagnostics, while semantic-quality gains remain an empirical hypothesis requiring calibration. We also pro- vide implementation-oriented interfaces and a worked compositional example to support staged empirical validation on NISQ-era hardware.

Abstract: Contemporary theories of emotion explain affective life through biologically prepared modules, cognitive appraisal mechanisms, predictive construction, dimensional organization, neural survival circuits, attachment regulation, adaptive computation, and error minimization. While each framework offers important insight, none fully explains why emotional intensity varies dramatically across contexts, why identical informational input yields divergent responses, or why certain commitments reorganize identity and override instrumental reasoning.This paper introduces the Dynamic Love-Based Valuation (DLBV) framework, proposing that emotional diversity emerges from a unified valuation architecture grounded in identity-relevant valuation, termed love. Love is defined not as romantic behavior nor as a discrete emotion, but as a structural valuation system operating across three qualitatively distinct phases: attraction, immersion, and union. Attraction preserves instrumental rationality and goal-directed evaluation. Immersion reorganizes valuation through identity expansion and potential subordination of rational constraint. Union stabilizes valuation within integrated attachment and responsibility.Within this framework, emotions such as fear, anger, sadness, joy, jealousy, guilt, gratitude, hope, and despair are interpreted as contextual modulations of valuation under specific informational conditions, including threat, violation, deprivation, alignment, rivalry, uncertainty, or loss. Emotional tone and intensity depend not solely on appraisal content or arousal magnitude but on the structural depth at which valuation operates.To move beyond conceptual synthesis, the paper proposes an experimental paradigm designed to test phase-dependent modulation of emotional intensity. The design operationalizes identity-relevant valuation across the three love phases and examines whether identical informational stimuli elicit systematically different affective responses depending on phase-structured commitment. The Dynamic Love-Based Valuation framework therefore offers both an integrative theoretical account and a falsifiable empirical program for investigating the structural architecture of emotion.

Abstract: We report the first complete circular genome of Acetobacter cerevisiae KSO5, an indigenous strain isolated from Korean fruit vinegar, comprising a 3.3 Mb chromosome and two plasmids encoding 2,898 genes. Phylogenomics confirmed species assignment (average nucleotide identity, ANI 97%; digital DNA–DNA hybridization, dDDH 71%). Comparison with seven draft A. cerevisiae genomes revealed strain-specific genomic islands, mobile genetic elements and polymorphisms in stress-response pathways, with enrichment in acid-tolerance–associated functions, and highlighted plasmid-borne modules potentially linked to genetic stability. The genome encodes a periplasmic oxidative fermentation system with membrane-bound pyrroloquinoline quinone-dependent alcohol dehydrogenase (PQQ-ADH) and molybdopterin-dependent aldehyde dehydrogenase (Mo-ALDH), together with respiratory-chain components consistent with flexible aerobic metabolism. Three acetate-handling routes (efflux, acetyl-CoA conversion and an AarC branch) were also predicted, suggesting mechanisms to limit intracellular acetate accumulation. Consistent with these features, phenotyping under ethanol stress (5–10%) showed measurable growth and titratable acidity production up to 9% ethanol (late-stage peak acidity). These data provide a genomic and phenotypic basis for developing robust vinegar starter cultures.

Abstract: Background: Women with atrial fibrillation experience a higher lifetime risk of ischemic stroke, greater stroke severity, and worse functional outcomes than men. Preventive strategies focused on AF detection may therefore miss critical opportunities for early intervention in women; (2) Methods: We developed a decision-analytic Markov model using real-world primary care data from Catalonia (Spain) to evaluate an artificial intelligence (AI) enabled strategy for upstream thromboembolic risk detection. The intervention combined electronic health record–based risk prediction, targeted digital rhythm screening, and individualized anticoagulation. Lifetime clinical and economic outcomes were estimated for adults aged ≥65 years, with pre-specified sex-stratified analysis; (3) Results: Compared with usual care, the AI-enabled strategy reduced ischemic stroke, major adverse cardiovascular events, and long-term disability. Absolute reductions in stroke and disability were greater in women, reflecting higher baseline thromboembolic risk. Per 1,000 high-risk women, the strategy prevented more strokes and generated larger quality-adjusted life-year gains than in men. From both healthcare payer and societal perspectives, the intervention was cost-saving in women, driven by reductions in stroke-related disability and long-term care; (4) Conclusions: AI-enabled upstream thromboembolic risk detection may deliver particularly important benefits for older women and represents a promising approach to reduce sex-based inequities in stroke prevention.

Abstract: We study learnable spectral layers whose feature family is generated by an entire function, motivated by the Master Integral Transform (MIT) of [1]. In a periodic discrete model on T, we define an oversampled multi-β analysis operator A(M) g,K built from an entire generator g and show that, on the one-sided bandlimited subspace PWK (T), it is a tight frame with an explicit inverse, sharp frame bounds, and noise-stability constants governed solely by the Taylor coefficients of g. For general (non-bandlimited) signals we derive exact aliasing identities and quantify two de-aliasing mechanisms: a deterministic multi-resolution cancellation scheme and a randomized rotation estimator with unbiasedness, MSE = O(1 /m), and high-probability bounds. We extend the discrete theory to Td, allowing general multivariate entire generators G(z) = ∑α∈Nd cαzα, and obtain exact inversion and conditioning bounds on tensor bands PW(d) K (Td) with explicit constants. To connect discrete layers to continuum MIT injectivity, we formalize density control of active Taylor indices. We prove that bounded gaps imply positive one-sided interior Beurling–Malliavin density (hence, in particular, positive lower counting density), closing an end-to-end bridge from gap-regularized learning to the injectivity theorem of [1]. For bounded-gap sequences we also give a weighted-series characterization of strong a-regularity, yielding computable surrogate penalties. Finally, we prove two injectivity mechanisms that do not rely on density: (i) a β-analytic injectivity theorem (access to multiple β-channels near 0) for any nonconstant entire kernel, and (ii) a finite-band generic-shift result ensuring invertibility on PWK for nonpolynomial generators. Full-data experiments illustrate conditioning collapse without coefficient floors and confirm the predicted 1 /m de-aliasing variance decay. Contributions (take-home). (i) Certified band-invertible spectral layers: exact inversion + frame bounds + noise stability on PWK (T) and PW(d) K (Td) with constants controlled by Taylor coefficients; (ii) Provable de-aliasing: deterministic multi-resolution cancellation and randomized rotation Monte Carlo with unbiasedness, MSE = O(1 /m), and high-probability bounds; (iii) A closed discrete-to-continuum bridge: bounded-gap activity ⇒ DBM > 0 and hence the lower counting density required by MIT injectivity; (iv) Beyond-density injectivity + nonharmonic structure: two density-free injectivity mechanisms and a monomial rigidity principle. Applications. These results yield MIT-certified modules for machine learning and scientific computing: invertible FFT-like embeddings for grid data, learnable positional encodings, and drop-in replacements for Fourier blocks in neural operators with explicit conditioning control and provable de-aliasing.

Abstract: This paper offers estimates of the per-capita GDP growth trajectories consistent with zero CO₂ emissions. The focus is on six developed economies (Canada, Germany, Italy, Japan, Singapore, United States) and six emerging economies (China, India, Indonesia, Malaysia, Mexico, South Korea). To this end, we postulate model linking per-capita CO₂ emissions growth to per-capita GDP growth and technology. Parameter estimation relies on annual data from 1980 to 2022 using Ordinary Least Squares (OLS) and Instrumental Variables (IV). Given the parameter estimates, we use the model to estimate the growth rate of per-capita GDP that is consistent with zero growth in CO2 emissions.

Abstract: Bell tests and Bell's theorem used to interpret the test results opened the door to quantum information processing, such as quantum computation and quantum communication. Based on the erroneous interpretation of the test results, quantum information processing contradicts a well-established mathematical fact in point-set topology. In this study, the feasibility of quantum computation and quantum communication is investigated. The findings are as follows. (a) Experimentally confirmed statistical predictions of quantum mechanics are not evidence of experimentally realized quantum information processing systems. (b) Physical carriers of quantum information coded by quantum bits (qubits) do not exist in the real world. (c) Einstein's ensemble interpretation of wave-function not only will eliminate inexplicable weirdness in quantum physics but also can help us see clearly none of quantum objects in the real world carries quantum information. The findings lead to an inevitable conclusion: Without carriers representing quantum information, physical implementations of quantum information processing systems are merely an unrealizable myth. Examples are given for illustrating the reported results. For readers who are unfamiliar with point-set topology, the examples may alleviate difficulty in understanding the results.

Abstract:

Background. Transient ischemic attack (TIA) and minor stroke often result in excellent functional recovery but are frequently followed by substantial psychological morbidity. It remains unclear whether mood disturbances or cognitive impairment are the primary contributors to reduced health-related quality of life (HRQoL) in this population. Methods. We conducted a prospective observational case–control study including 90 patients with acute TIA or minor stroke confirmed by diffusion-weighted imaging, and 92 age-matched healthy controls. At 90 days, participants completed the Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, Montreal Cognitive Assessment, and the EQ-5D-5L. Hierarchical multiple regression using standardized z-scores identified independent predictors of HRQoL. Bias-corrected bootstrapped mediation analyses (5,000 iterations) assessed whether cognitive impairment mediated the relationship between mood symptoms and HRQoL. Results: Compared with controls, patients exhibited markedly higher rates of depression (82.2% vs. 18.5%), anxiety (81.1% vs. 21.7%), and cognitive impairment (66.7% vs. 13.0%) (all p<0.001). Psychopathological variables explained an additional 36.6% of HRQoL variance, whereas cognitive and neuroimaging variables contributed only 1.7% (ΔR²=0.017; p=0.523). Anxiety showed the strongest predictive value (β=–0.055; p=0.064), while cognitive impairment had negligible effects (β=–0.001; p=0.947). Mediation analyses revealed no significant indirect effects, indicating that mood and cognitive complications arise independently rather than sequentially. Conclusions: Following TIA or minor stroke, depressive and anxiety symptoms are highly prevalent, persist despite good neurological recovery, and exert a disproportionately negative impact on HRQoL. Anxiety appears particularly influential in determining patient-reported outcomes, underscoring the need for routine mood screening and targeted psychological management in this population.

Abstract: The rapid development of artificial intelligence (AI) has increasingly influenced educational practices, including English language teaching. However, the effectiveness of AI integration in classrooms largely depends on teachers’ perceptions and their ability to incorporate these technologies into pedagogical practices. This study explored teachers’ perspectives on the implementation of AI tools in English language teaching in Indonesian classrooms, focusing on perceived benefits, challenges, and implications for instructional practice. A qualitative research design was employed to capture teachers’ experiences and viewpoints. Data were collected through semi-structured interviews with English teachers who had experience using AI-based tools, supported by document analysis of instructional materials. The data were analyzed using thematic analysis to identify recurring themes. The findings indicated that teachers generally perceived AI tools as supportive resources that enhanced language learning, particularly by providing immediate feedback on students’ writing, supporting vocabulary development, and assisting in the preparation of learning materials. AI applications also encouraged students to engage more actively in revision and language practice. Nevertheless, teachers expressed concerns about students’ potential overreliance on AI-generated responses and emphasized the importance of guiding learners to critically evaluate AI feedback. In addition, differences in digital literacy, institutional support, and technological infrastructure influenced the extent to which AI tools were effectively integrated into classroom practices. The study concludes that AI technologies can contribute to more flexible and learner-centered language learning environments when used thoughtfully and supported by appropriate pedagogical strategies and institutional policies.

Abstract: Background: Drug-resistant tuberculosis (DR-TB) remains a critical public health challenge in South Africa, particularly in rural areas with high HIV prevalence. This study aimed to evaluate treatment outcomes and identify risk factors associated with unfavourable outcomes among DR-TB patients in the rural Eastern Cape using survival analysis. Methods: A retrospective cohort study was conducted using data from 323 patients diagnosed with DR-TB and treated between February 2018 and December 2021. Patient demographics, clinical characteristics, and treatment outcomes were extracted from medical records. Kaplan-Meier survival estimates were used to analyse time-to-event data, and Cox proportional hazards regression was used to identify predictors of treatment outcomes. Variables with p < 0.1 in univariate analysis were included in the multivariate model; statistical significance was set at p < 0.05. Results: The median treatment duration was 10 months (IQR: 9–11). The overall cure rate was 36.2% (n=117), treatment completion 26.0% (n=84), LTFU 9.0% (n=29), treatment failure 2.2% (n=7), death 9.3% (n=30), and transfer-out 9.3% (n=30); 8.1% (n=26) were still on treatment. HIV co-infection was present in 62% of patients and was associated with higher mortality (86% of deaths) and treatment failure (86%). In multivariate analysis, primary education (HR = 0.393, 95% CI: 0.23–0.68, p = 0.0017) and secondary education (HR = 0.504, 95% CI: 0.31–0.85, p = 0.0103) were protective. Pre-XDR (HR = 0.134, 95% CI: 0.03–0.81, p = 0.034) and XDR-TB (HR = 0.164, 95% CI: 0.03–0.94, p = 0.043) were unexpectedly associated with lower hazard, likely due to early mortality or transfer. HIV-negative status was linked with a higher hazard (HR = 1.735, 95% CI: 1.13–2.66, p = 0.010). Conclusion: Treatment success rates remain suboptimal among DR-TB patients in the rural Eastern Cape. HIV co-infection, prior treatment history, and low education levels were associated with unfavourable outcomes. Survival analysis highlighted critical timeframes for intervention and retention in care. Targeted support for younger patients, males, and HIV-positive individuals is essential to improve DR-TB treatment outcomes in high-burden rural settings.

Abstract:

Introduction Overactive bladder (OAB) frequently co-occurs with cardiovascular-kidney-metabolic (CKM) syndrome; however, the complex interplay of systemic inflammation, psychological distress, and metabolic dysregulation driving this connection remains poorly defined. This study aimed to elucidate these multidimensional associations and identify shared metabolic patterns between OAB and CKM-related conditions. Methods We analyzed data from 11,836 participants in the National Health and Nutrition Examination Survey (2005–2018). CKM stages were classified using American Heart Association criteria, while OAB severity, systemic inflammation, and depression were assessed via the Overactive Bladder Symptom Score, neutrophil-to-high-density lipoprotein cholesterol ratio (NHR), and Patient Health Questionnaire-9, respectively. We utilized survey-weighted multivariable regression and mediation analysis. Furthermore, two-sample Mendelian randomization (MR) analyses using genome-wide association study datasets were conducted to identify causal metabolites. Results Higher CKM stages were significantly associated with increased OAB severity. Elevated NHR and depression scores were independently linked to OAB. Notably, a significant synergistic interaction was observed: moderate inflammation amplified the impact of depressive symptoms on OAB. Mediation analyses demonstrated that NHR, depression, and their interaction significantly mediated the relationship between CKM stage and OAB. MR analysis identified specific causal lipid, amino acid, and energy-related metabolites for OAB, exhibiting substantial overlap with CKM metabolic signatures. Discussion & Conclusion CKM progression, systemic inflammation, and depression are robustly associated with OAB, linked through neuro-inflammatory and psychological pathways. OAB appears to be a manifestation of systemic dysregulation shared with CKM syndrome, necessitating integrated management strategies addressing cardiometabolic health and psychological well-being.

Abstract: The rapid expansion of mining activities in the Katangan Copperbelt has led to the accumulation of large volumes of mine waste dumps, which increasingly structure extractive landscapes. However, their spatial dynamics and morphological evolution remain insufficiently documented. This study analyses the spatio-temporal evolution of mine waste dumps in Lualaba Province (Democratic Republic of the Congo) between 2009 and 2025 in order to characterize their growth patterns, morphological changes, and spatial organization. Mine waste dumps were mapped through multi-temporal interpretation of high-resolution imagery in Google Earth Pro and analysed using GIS-based spatial metrics and statistical approaches. Results reveal a strong increase in dump area from approximately 1,900 ha in 2009 to more than 6,400 ha in 2025. The dynamics shift from a phase dominated by the proliferation of dumps between 2015 and 2020 to a phase characterized by the expansion and consolidation of existing deposits after 2020. Mutshatsha territory emerges as the main hotspot of mining intensification, while Lubudi territory displays more irregular dynamics and stronger morphological changes. Spatial metrics indicate a clustered distribution of dumps around active mining areas, followed by a partial spatial expansion toward new zones after 2020. Although most dumps occur relatively close to the road network, statistical analyses show that transport accessibility has only a limited influence on their size or emergence. Overall, these results highlight the importance of morpho-spatial monitoring of mine waste dumps for understanding mining landscape transformations and for supporting the spatial prioritization of ecological remediation strategies.

Abstract: Context sensitivity in large language models (LLMs) is typically treated as a single dimension — models either "use context" or they do not. We challenge this view by decomposing context sensitivity into two measurable, independent dimensions: structure and trajectory. Using the Relational Coherence Index (RCI) framework and TRUE/SCRAMBLED/COLD experimental conditions, we analysed the conservation-validated model subset from Paper 6 (N = 8 Medical, N = 6 Philosophy) using TRUE/SCRAMBLED/COLD experimental conditions. Content-Order decomposition demonstrates that medical reasoning is 61% content-driven while philosophical reasoning is 59% order-driven (Mann-Whitney U = 45, p = 0.0047, Cohen's d = 1.59). Both content and order increase ΔRCI but have opposite effects on variance: content amplifies variance 4–6× while order suppresses it to ~30% of baseline. This variance machinery is domain-invariant (p = 0.463; p = 0.867), with domain specificity residing entirely in ΔRCI. Exploration Arc analysis reveals complete domain separation: philosophy expands conceptual diversity over conversation (mean Arc = 15.2) while medical responses remain stable (mean Arc = 1.7), with zero overlap between domains. A pilot analysis of Context Utilization Depth (CUD) is reported in Supplementary Material S1; three of four tested models show full recency dominance (CUD = 1), with Llama 4 Maverick as the sole exception (82%→98% K-curve). These findings provide a structural account of how LLMs process conversation, with direct implications for RAG design, prompt engineering, and clinical AI safety.

Abstract: Land degradation caused by soil erosion is a major challenge in Mediterranean sloped agroecosystems, where extreme weather events and conventional land management practices accelerate soil loss and threaten long-term sustainability. This study evaluates and compares three complementary approaches to estimate soil erosion in an olive orchard in Messenia, Greece. Field-based runoff plots provided direct measurements of sediment yield, drone-based LiDAR surveys enabled soil surface change detection through the Difference of Digital Elevation Models (DoD) method, and the Revised Universal Soil Loss Equation (RUSLE) was applied to model erosion risk using site-specific parameters. Results indicate that field measurements and RUSLE estimates are broadly consistent, particularly when the model is calibrated with empirical data, offering reliable insights into soil loss dynamics. In contrast, the LiDAR–DoD approach was used to characterize soil surface displacement rather than to directly quantify soil erosion. Due to methodological and technical limitations, LiDAR–DoD results are presented primarily as a framework for future research rather than as a definitive erosion assessment tool. Overall, the integration of field monitoring, remote sensing, and modeling highlights the strengths and limitations of each method and demonstrates the value of multi-method approaches for improving erosion assessment and supporting sustainable land management in vulnerable Mediterranean landscapes.

Abstract: We present a complete, parameter-free derivation of the bandlimited Green's function G = \sin (\sqrt{- \sigma^2}) and the celestial conformal weights \Delta_l = l + 1 from a single input: four-dimensional Minkowski spacetime (M,\eta_{\mu \nu}). The derivation proceeds in three steps. First, the exactness of the exponential map \exp_p: T_p M \to M in flat spacetime, combined with the requirement that discrete sampling be isometrically equivalent to the continuous field, uniquely determines—via the Whittaker interpolation theorem—the reproducing kernel G = \sin (\Omega \sqrt{- \sigma^2}). Second, the null geodesic locus \sigma^2 = 0 emerges as the natural boundary through the reproducing kernel normalisation condition K(x,x) = 1; restriction to this null hypersurface induces a signature flip from Lorentzian (- , + , + , +) to Euclidean (+, +) on the transverse S^2. Third, the SL(2,\mathbb{C}) principal-series representation on the Euclidean celestial sphere, combined with the spherical Bessel decomposition of G, yields \Delta_l = l + 1 as a pure spectral theorem with no free parameters. The result is cross-validated by five independent routes: Kempf's operator-theoretic reconstruction, the present geometric construction, a boundary RKHS derivation, Pasterski-Shao-Strominger from scattering amplitudes, and Gover-Shaukat-Waldron tractor calculus providing the SO(4,2) group-theoretic skeleton explaining why all five routes converge. The scale \Omega is structurally irrelevant: all physical conclusions depend only on the Minkowski metric. We identify the null-geodesic data set as a natural basis for geometric consistency checks, and note that if the universe is a quantum state, the multi-path convergence in principle circumvents the classical cosmic variance bound.