Abstract: Identifying the main drivers of soil CO₂ emissions in tropical agroecosystems is essential for balancing productivity and climate mitigation. This study evaluated the effects of crop type, irrigation, phenological stage, and fertilization on soil respiration in a humid marshland system in Rwanda using a two-season field experiment. Five crops (maize, soybean, common bean, Irish potato, and Brachiaria) were grown under irrigated and rainfed conditions, and soil CO₂ emissions were measured across 19 sampling campaigns in both crop-covered and adjacent bare soil conditions in all plots. Crop type and growth stage were the dominant drivers of soil CO₂ emissions (p < 0.001), while irrigation had no significant direct effect despite increasing yields (p < 0.001). As a result, irrigation reduced yield-scaled CO₂ emissions for several crops (p < 0.05–0.01). Brachiaria showed higher emissions, particularly during the development stage, but its high bio-mass led to lower emissions per unit yield. Fertilization significantly increased soil respiration (p < 0.001), and emissions were higher under crop-covered soils than bare soils (p < 0.001). These findings indicate that crop traits and nutrient inputs primarily control soil respiration under moisture-sufficient tropical conditions.

Abstract: For 24 of the 69 chemicals measured in U.S. National Health and Nutrition Examination Survey (NHANES) urine biomonitoring with data for both children aged 3–5 and adults aged 66 and older — including di-2-ethylhexyl phthalate (DEHP) and inorganic tin — no single regulatory exposure standard can be simultaneously epistemically grounded for both populations. This finding, which we term severe regulatory incommensurability, cannot be obtained from Bayesian inference or any significance test: it requires a geometric measure of the overlap between population-specific feasibility regions that has no probabilistic analog.We derive this result by applying the Theory of Epistemic Abductive Geometry (TEAG) — a possibilistic, constraint-based inference framework grounded in possibility theory and tropical mathematics — to the complete 179-chemical, 11-demographic-group dataset of Stanfield et al. (2022), the gold-standard Bayesian biomonitoring pipeline. TEAG recovers Bayesian median intake rate estimates with near-perfect agreement (r = 0.9965, RMSE = 0.15 log₁₀), establishing that the two frameworks agree on point estimates while diverging fundamentally on the geometric structure of the inference.The primary findings are: (i) the κ pairwise overlap coefficient is below 0.5 for 24 chemicals, meaning no intake rate achieves simultaneous epistemic feasibility above 50% for both age groups, with child-to-elder fold differences up to 8.6×; (ii) the TEAG admissible epistemic basin is on average 20.3× narrower than the Bayesian 95% credible interval, reflecting the geometric separation of measurement censoring, metabolite ambiguity, and demographic variability rather than false precision; (iii) demographic groups can be ordered by falsification priority — children aged 3–5 rank first at 1.89× mean distance from the committed population estimate; and (iv) 70% of 138 chemicals with longitudinal NHANES data (1999–2016, 9 cohorts) undergo epistemic phase transitions across cohorts, with atrazine mercapturate showing a 1.21 log₁₀ commitment reversal and arsenous acid — a severely incommensurate chemical — undergoing a persistent PCRB status change beginning in 2011–2012. A formal proof establishes that the κ incommensurability coefficient cannot be reproduced from any function of Bayesian posterior summary statistics, even given identical posterior means, variances, and credible interval widths.We call explicitly for population-differentiated reference doses for the 24 severely incommensurate chemicals and propose that κ < 0.5 between children and elderly adults in NHANES biomonitoring data be adopted as a standing geometric criterion for triggering age-stratified regulatory review.

Abstract: Reinforcement learning (RL) in mobile edge computing (MEC) faces critical challenges of data heterogeneity, communication overhead, and limited generalization across diverse preferences and system configurations. We propose Adaptive Reinforcement Learning Offloading (ARLO), a unified framework integrating adaptive dissimilarity measures for federated learning with generalizable multi-objective optimization for computation offloading. The Adaptive Dissimilarity Measure module leverages parameter dissimilarity with Lagrangian multipliers to mitigate model drift under Non-IID data and loss dissimilarity to reduce communication overhead via adaptive aggregation. The Contextual Multi-Objective Decision module employs histogram-based state encoding and a Generalizable Neural Network Architecture with action masking, enabling a single policy to adapt to varying preferences, server counts, and CPU frequencies. Experiments show ARLO achieves 82.6% accuracy on CIFAR-10 with 44.3% fewer communication rounds than FedProx, and a 121.0% hypervolume improvement in offloading with only 1.7% generalization error across unseen configurations.

Abstract: We prove a what we call a general covariance theorem for entire-function deformations of relativistic field theories, the result is that if an undeformed theory is covariant or invariant under some symmetry group, and the deformation is defined by an entire function of a differential operator that intertwines the symmetry group-action, then the deformed theory remains covariant or invariant with respect to that same symmetry group. We will establish this result in both abstract and geometric forms and derive corollaries for Poincar\'e symmetry, gauge covariance, and continuum diffeomorphism covariance. In the gauge-fixed sector we as well prove a finite-dimensional BRST theorem with Ward and Slavnov--Taylor identities for admissible truncations in the theory.

Abstract: Nigeria faces a staggering housing deficit currently estimated at between 22 million and 28 million units, a crisis that has evolved from a simple shortage of units into a broader failure of habitability. This study assesses the residential dynamics of Oyo State, with specific focus on urban pressures in Ibadan and Ogbomoso. Using a review of current literature and recent case studies from the University of Ibadan's Department of Architecture, the research examines structural, economic, and legislative barriers to adequate housing. The methodology involves an analysis of historical urban morphology, sustainable material science, including the use of sawdust, Bamboo Leaf Ash (BLA), and Palm Kernel Shell Ash (PKSA), and the impact of the 1978 Land Use Act. Results indicate that while rapid urbanization, with Ibadan exceeding 4 million residents, has outpaced formal housing delivery, innovative solutions including the Millard Fuller Foundation's incremental housing model and Construction 5.0 technologies offer clear pathways to affordability. The study concludes that resolving the crisis requires decentralized land governance, the adoption of locally sourced sustainable materials, and a focus on community-centered design to ensure long-term urban resilience.

Abstract: We investigated the structural, magnetic, magnetocaloric, and magnetotransport properties of Ni₅₀Mn₃₅In₁₅ Heusler alloys by partial substitution of Ni with 3 at.% Bi (Ni₄₇Bi₃Mn₃₅In₁₅) and Si (Ni₄₇Si₃Mn₃₅In₁₅) synthesized by arc-melting. X-ray diffraction confirms a predominant L₂₁ cubic structure (space group Fm-3m), while SEM/EDX verifies compositional homogeneity. Temperature-dependent magnetization measurements reveal that the Bi-substituted alloy exhibits a first-order magnetostructural transition associated with the martensitic transformation, followed by a second-order ferromagnetic–paramagnetic transition near the Curie temperature. In contrast, the Si-substituted alloy shows a single second-order transition with negligible thermal hysteresis, indicating suppression of the martensitic phase. The Curie temperature decreases from 324 K for the parent alloy to 313 K and 286 K for the Bi- and Si-substituted alloys, respectively. A maximum magnetic entropy change of 6.0 Jkg^-1K^-1 and 4.5 Jkg^-1K^-1 is obtained under an applied magnetic field change of 50 kOe for the Bi- and Si-substituted alloys, respectively, with corresponding relative cooling power values of 303 Jkg^-1 and 345 Jkg^-1. These results demonstrate that lattice expansion (Bi) and contraction (Si) distinctly modify Mn–Mn exchange interactions, enabling tunable magnetocaloric performance in Ni–Mn–In Heusler alloys.

Abstract: The Fenton reaction remains one of the most widely investigated advanced oxidation processes for wastewater treatment due to its ability to generate highly reactive oxygen species capable of degrading persistent organic pollutants. However, classical homoge-neous Fenton systems suffer from significant limitations, including narrow pH applica-bility, iron sludge generation, and poor catalyst reusability. In response, extensive research has been devoted to the development of heterogeneous and advanced Fenton-like catalysts that address these challenges while improving catalytic efficiency and operational stabil-ity. This review provides a comprehensive analysis of the evolution of Fenton catalysis, from classical homogeneous systems to modern advanced materials, including nanostructured catalysts, carbon-based Fe–N–C systems, metal–organic frameworks, and single-atom catalysts. Particular emphasis is placed on key performance parameters such as catalytic activity, manufacturability, stability, and catalyst lifespan. A critical comparison of these systems highlights the trade-offs between activity, cost, and scalability, demonstrating that the most advanced catalysts do not necessarily offer the best practical performance. A dedicated life cycle assessment perspective is included, focusing on catalyst lifespan, reuse efficiency, and iron leaching, providing quantitative insights into long-term sus-tainability. The analysis reveals that while advanced catalysts significantly improve cu-mulative catalytic output, their environmental and economic viability depends on synthe-sis complexity and durability under realistic conditions. Finally, current challenges and future directions are discussed, including the need for scalable synthesis methods, improved mechanistic understanding, and integration into hybrid treatment systems. This review aims to bridge the gap between fundamental re-search and practical application, offering guidance for the design of next-generation sus-tainable Fenton catalysts for wastewater treatment.

Abstract: A genuinely responsive virtual cell must capture the nonequilibrium dynamics of living systems, not merely infer cellular states through statistical projection. This in turn requires balanced physical fidelity between the representation of interior dynamics and environmental coupling. Standard thermostats and barostats are phantom baths: algorithmic reservoirs that impose instantaneous, global control in place of physically mediated exchange. They thereby distort broad classes of cellular processes that depend on localized transport of heat, momentum, and matter. As path-integral methods, AI force fields, and quantum computing push interior fidelity to progressively higher levels of accuracy, this imbalance becomes increasingly consequential. To clarify what is at stake across the full range of virtual-cell approaches, this perspective introduces the physical-fidelity continuum, spanning predictive-statistical models, mechanistic dynamical simulations, and the physical-accuracy limit set by quantum-computational approximations and available experimental validation. A focus-dynamic hybrid architecture and benchmark hierarchy are proposed as the constructive framework for achieving and verifying balanced physical fidelity.

Abstract: This observational study evaluated changes in selected performance parameters of 15 new high-speed dental handpieces after eight months of routine clinical use in a routine educational undergraduate environment (two 4h daily clinical shifts, five days per week, with repeated sterilization cycles). All handpieces underwent routine cleaning, lubrication, and autoclave sterilization as instructed. The turbine components from the handpieces were disassembled and examined by stereomicroscopy before and after use, while free-running speed and bur-tube friction grip force were assessed at the same intervals. Two handpieces were no longer operational at follow-up due to ball bearing failure. Among the remaining handpieces, statistically significant reductions were observed in both free-running speed and friction grip force (p &lt; 0.01). Microscopic examination of the rotors revealed surface alterations consistent with corrosion and wear. Within the limitations of this study, routine clinical use over an eight month period was associated with measurable changes in key performance characteristics of high-speed dental handpieces in educational clinical settings.

Abstract: Unmanned aerial vehicles (UAVs) are increasingly recognized as a viable option for urban parcel delivery. However, their energy performance under varying environmental and mission conditions remains underexplored. This paper presents a simulation-based analysis of multirotor UAV energy consumption using the PX4-Gazebo platform, calibrated with real-world telemetry from a publicly available DJI Matrice 100 dataset [12]. Three UAV models—Iris, Typhoon H480, and Octocopter—were evaluated across a range of payloads (0.1–5 kg), cruise speeds (2–16 m/s), and environmental factors, including wind, temperature, and humidity. Results revealed consistent U-shaped energy speed curves, with optimal cruise speeds ranging from 8 to 10 m/s, depending on the payload and platform. Headwinds alone increased energy consumption by up to 25% and combined cold–dry and headwind conditions resulted in increases of up to 53% for lightweight platforms. Validation against field telemetry showed mean absolute percentage errors below 11%. These findings offer a simulation-grounded framework for UAV mission planning, platform selection, and integration into energy-efficient logistics networks, and development of data-driven optimization frameworks. The three platforms span a 10-fold mass range (1.4–14 kg), enabling systematic analysis of how energy scaling behavior varies across lightweight, mid-range, and heavy-lift delivery configurations.

Abstract: Gambling disorder (GD) is associated with severe psychosocial impairment, impulsive dyscontrol, affective instability, and increased suicidal risk. Pharmacological options remain limited, particularly for patients whose presentation is dominated by transdi-agnostic dimensions such as impulsivity, emotional dysregulation, and suicidal vul-nerability. Lithium may be relevant because of its anti-suicidal properties and potential effects on affective instability and behavioral dyscontrol. We describe a 40-year-old man with sports-betting-related GD who presented after a suicidal crisis in the context of fi-nancial collapse and marital conflict. He entered a structured multimodal program in-cluding psychiatric care, lithium carbonate, individual psychotherapy, psychoeduca-tional and self-help groups, family intervention, and social support. After lithium initi-ation and titration to 600 mg/day, the patient showed progressive affective stabilization, remission of suicidal ideation, reduced gambling urges, and sustained abstinence, with one brief lapse that was rapidly contained. Psychometric reassessment paralleled the clinical course, showing reduced functional impairment, near-complete resolution of gambling-related cognitive distortions, and a shift from a highly impulsive/dysregulated gambling profile toward an emotionally vulnerable pattern. Although causal inference is limited by the single-case design and multimodal treatment context, this case supports the hypothesis that lithium may help stabilize core vulnerability dimensions in selected GD presentations, particularly impulsivity, affective dysregulation, and suicidality.

Abstract: The use of copaiba oil (COP) in ruminant nutrition is relatively recent, and results reported in the literature are still controversial. This study evaluated the effects of different concentrations of copaiba oil in the diet of steers on ruminal fermentation. Five rumen-cannulated steers were assigned to a 5 x 5 Latin square design and subjected to the following treatments: Control – (0 g of COP), 1.25 g COP, 2.50 COP, and 3.75 g COP kg^-1 dry matter (DM), and monensin (positive control - concentrate containing 40 mg kg^-1 DM). Animals were fed a diet with a forage-to-concentrate ratio of 50:50. Inclusion of copaiba oil from 1.25 to 3.75 g kg^-1 did not alter ruminal pH, and the concentrations of NH₃-N and propionate (mmol L^-1) were similar among treatments (P > 0.05). Copaiba oil did not affect intake, digestibility, or propionate concentration (P > 0.05). Monensin increases (P ≤ 0.05) the concentrations of NH3-N and propionate (mmol L^-1). Different concentrations of copaiba oil (Copaifera spp.) in the diet of steers did not affect ruminal fermentation. However, additional studies are needed to evaluate the inclusion of COP in diets with a higher forage proportion, better representing grazing conditions with predominant forage intake.

Abstract: Obesity represents a significant public health concern, attributable to its high preva-lence and its association with cardiometabolic comorbidities. This study compared a set of ensemble learning models—including canonical ensembles, meta-ensembles, and base-lines for tabular data—in a multiclass obesity status prediction task using the “Obesity Dataset” (n = 1,610; 14 predictors; 4 classes). To ensure methodological rigor, a pipeline was implemented using ColumnTransformer, standardization, one-hot encoding, and re-balancing via SMOTENC applied exclusively to the training folds, thereby preventing data leakage. The performance of the system was evaluated using several evaluation metrics, including accuracy, F1-score, precision, recall, Cohen's kappa, and Matthews correlation coefficient. This evaluation was supplemented by a computational cost analysis. Inferen-tial comparisons were executed using the Friedman test and the Nemenyi post-hoc test (α = 0.05). The findings indicated a high level of overall performance (≈89–90.5% precision), identifying a leading group of models that were statistically indistinguishable (Group A). This group included LightGBM (90.49% ± 1.38), Random Forest (90.16% ± 1.70), Stacking (90.21% ± 1.70), and Extra Trees (89.69% ± 1.55). It has been demonstrated that models such as XGBoost, Bagging, and CatBoost demonstrate competitive performance with par-tial statistical overlap. Conversely, Gradient Boosting and AdaBoost exhibited signifi-cantly lower performance. In summary, a single dominant model was not identified; ra-ther, a set of equivalent solutions was identified. The selection of a model should be based on a balance between accuracy, computational cost, and interpretability. Random Forest and Extra Trees are efficient options, and Stacking is a valid alternative when maximizing predictive performance is prioritized.

Abstract: While falling costs have expanded access to genomic sequencing, clinical utility is frequently hindered by the challenge of interpreting complex genetic data. Although advances in genetic variant classification have improved diagnostic precision, they have also increased the identification of variants of uncertain significance (VUSs), widening the interpretation gap between data generation and clinical actionability. The high prevalence of VUSs can lead to false reassurance or psychological distress, as patients and non-expert clinicians may misinterpret inconclusive results. We propose that artificial intelligence (AI) is a critical clinical decision-support tool for bridging this gap, offering a scalable framework to optimize variant interpretation and shorten the diagnostic odyssey. We advocate integrating AI throughout the genetic diagnostic workflow–from initial phenotyping to variant prioritization–to facilitate data-driven, personalized treatment. We outline current AI-assisted approaches and discuss anticipated challenges in this pursuit, such as privacy, training data bias and quality, model explainability, and the necessity of a total product life cycle for validation. To address these challenges, we provide recommendations to ensure AI tools meet the highest standards of precision, reproducibility, and transparency. By standardizing AI across the variant analysis pipeline, we can fast-track the path to genetic diagnoses, effectively bridging the interpretation gap and enabling rapid delivery of personalized medical interventions.

Abstract: We address the problem of determining whether a given mixed quantum state corresponds to thermal equilibrium or to a zero-temperature statistical mixture. We show that geometric observables, in particular the quantum Fisher information, provide a direct diagnostic criterion. Thermal states satisfy fluctuation–response relations linking energy variance to parameter sensitivity, while generic mixed states do not. This establishes a geometric test of thermality that does not require prior knowledge of the Hamiltonian and connects equilibrium statistical mechanics with quantum information geometry.

Abstract: The early turbulence phenomenon has been observed in pipe flow of very dilute polymer solutions [1–4], and the full chord laminar flow can be achieved on various laminar suction wings at high Reynolds numbers (Re) up to approximately 2 000 000 [5–7]. Their transition conditions deviate significantly from the traditional criteria, critical Re about 2 000~2 300, which is quoted in most contemporary textbooks for pipe flow [8–13]. In this paper, a new force model with a virtual fluid layer, which is of a hemispherical shell shape and with a constant thickness inside a laminar pipe flow is established, on the basis of the membrane force model of a spherical shell under uniformly distributed load conditions in structural mechanics. In laminar flow state with a lower Re and a lower pressure gradient, the curvature radius of the virtual spherical liquid layer is inversely proportional to the pressure gradient. As Re increases, pressure gradient also increases, while the curvature radius decreases. When the curvature radius decreases to be equal to and starting less than the pipe radius, the stable liquid layer structure collapses, and the laminar flow becomes turbulent. This is a transition state with a critical tensile force flow defined as twice the product of the viscosity of the fluid and the maximum velocity in pipe, divided by the pipe radius. In laminar flow situation, the shear stress at the pipe wall can be interpreted as a horizontal component of the critical tensile force flow, and the direction is against the flow. Only when the flow achieving the transition condition, the shear stress at the wall become the critical tensile force flow itself, which had already been observed in early turbulence [1–4,14]. The second case, which can be explained by the concept of critical tensile force flow, is high Re laminar pipe flow [5–7], for example, the pipe with surface suction can be considered as a part of a virtual, larger pipe with a no slip wall at where the shear stress coincides with the critical tensile force flow, the shear stress at the real pipe is smaller, with a weakening factor related to the ratio of the average velocity in the real pipe to its maximum velocity.

Abstract: Objective: This study aims to conduct a comprehensive pharmacological analysis of a medical booklet identified in a personal library in Ganja, Azerbaijan. The text is written in Azerbaijani Cyrillic script, implying a mid-20th-century (Soviet-period) origin. The study specifically isolates, translates, and evaluates four distinct therapeutic claims found within the text: a Peganum harmala and grape molasses practice for psychiatric disorders; a Lawsonia inermis and sugar decoction for gangrene; a Lens culinaris regimen for pain; and a Ficus carica latex application for epistaxis. Material and Methods: The study employs a trans-disciplinary approach combining philological analysis of the source text with modern pharmacognosy, toxicology, and clinical simulation. The booklet’s citations—including The Canon, Ghayat al-Bayan, and Nüsrət Əfəndi—were analyzed to establish the intellectual lineage of the text. The four identified practices were deconstructed into their phytochemical constituents. Efficacy and safety profiles were modeled based on current database analyses of active metabolites (e.g., harmine, lawsone, ficin, polyphenols) and their bioavailability when processed according to the specific instructions. Results: The analysis reveals that the rue and doshab practice for mental illness utilizes an acid-base extraction method to maximize the bioavailability of beta-carboline alkaloids, functioning as a potent monoamine oxidase inhibitor. The fig stick method for epistaxis operates via enzymatic coagulation (factor X activation by ficin) and chemical cautery. The lentil practice for finger pain targets inflammatory pathways via polyphenolic inhibition of COX-2. Conversely, the henna–sugar practice for gangrene appears scientifically contraindicated and may carry a risk of oxidative hemolysis. Conclusion: The booklet represents a sophisticated synthesis of humoral (Galenic) and iatrochemical (Paracelsian) medicine. While certain practices demonstrate a rational pharmacological basis prefiguring modern drug delivery systems, others pose risks. This analysis underscores the necessity of rigorous toxicological screening before reviving traditional ethnomedical practices.

Abstract: A fundamental limitation in human–AI systems lies not only in how decisions are produced, but in how they are cognitively understood. While existing research has advanced models of trust, performance, and human–AI interaction, it provides limited conceptual tools for explaining how individuals construct meaning within system-mediated environments. This gap suggests that the challenge of human–AI integration is not only computational, but fundamentally conceptual.This paper develops a structured conceptual framework of conscious leadership to organize the cognitive processes through which individuals interpret, engage with, and act within AI-supported systems. Rather than introducing isolated definitions, the framework is articulated as an interconnected system of constructs that collectively shape perception, interpretation, and decision coherence.Building on prior work on perceptual integrity as a condition of cognitive coherence, the study identifies and integrates a set of foundational constructs, including cognitive balance, meaning gap, leadership latency, and cognitive governance. These constructs are positioned within a unified cognitive architecture that explains how meaning is formed, disrupted, and restored in human–AI interaction.The paper makes three contributions. First, it reframes leadership as a cognitive–interpretive system rather than a purely behavioral or relational construct. Second, it introduces a structured framework as a methodological tool for analyzing and designing human–AI systems. Third, it provides a foundation for future empirical research by defining constructs that can be operationalized and tested across contexts.As intelligent systems increasingly shape decision environments, structuring how meaning is constructed becomes as critical as optimizing decisions. A decision may be technically correct yet cognitively unintegrated. This study positions conceptual structure not as a descriptive layer, but as an active mechanism shaping cognition, leadership, and human–AI coherence.

Abstract: Urban water systems are increasingly challenged by climate change, population growth, and resource scarcity, requiring a shift from centralised, supply-oriented models to decentralised, resilience-based approaches. While energy transition policies have successfully promoted Nearly Zero-Energy Buildings (NZEB) and Renewable Energy Communities (REC), similar concepts for water management remain underdeveloped. This study proposes adapting these energy-based frameworks to the water sector through the concepts of Nearly Zero-Water Buildings (NZWB) and Urban Water Communities (UWC). A structured literature review is combined with a quantitative water balance analysis to evaluated the potential for reducing potable water demand through efficiency measures, greywater reuse, rainwater harvesting, and alternative local renewable sources. Results indicate that potable water consumption in residential buildings can be reduced by 53–100% depending on system configurations and local resources availability. Extending these strategies from building-scale solutions to district scale through water communities enhances system redundancy, flexibility, and adaptive capacity. The study further discusses the integration of decentralised water systems with smart city frameworks, highlighting the role of hybrid infrastructures in improving urban resilience. The findings demonstrate that decentralised and circular water strategies can play a key role in enabling sustainable, climate-adaptive, and smart urban environments, while also identifying regulatory and governance challenges for large-scale implementation.

Abstract: UK forestry faces increasing drought risk under climate change, raising concerns about the resilience of Sitka spruce, the UK’s dominant commercial conifer. This study assessed whether hyperspectral vegetation indices can detect intraspecific drought responses to support resilience screening. An eight-week controlled drought experiment was con-ducted on six clonal groups, using needle-level hyperspectral reflectance to derive indices of chlorophyll status, photoprotective pigments, and water content, alongside chlorophyll fluorescence (Fv/Fm). Drought responses were detected across multiple indices, with pigment-based and red-edge indices showing the earliest and strongest sensitivity, while water-related indices captured later-stage hydraulic decline. Significant clonal variation was observed in the timing and magnitude of pigment regulation, water loss, and photosynthetic impairment, indicating contrasting drought response strategies. These results demonstrate that hyperspectral approaches enable rapid, non-destructive detec-tion of physiologically meaningful drought responses and can support the identification of drought-resilient genotypes for climate-adaptive forest management.