Search Results (253)

Biosensing performance in graphene-derived field-effect transistors (BioFETs) is widely attributed to surface chemistry, yet the role of the underlying charge transport mechanism remains poorly understood. This work establishes a direct correlation between disorder-driven transport and biosensing transduction in vertical graphene (VG) and nanocrystalline graphite (NCG) FET devices. Temperature-dependent electrical characterization (15–500 K) reveals a hybrid transport regime: three-dimensional Mott variable-range hopping below 240 K, transitioning to thermally activated Arrhenius-type conduction above 240 K. The extracted VRH parameters characteristic temperature T₀, localization length ξ, and density of states N(E_F) quantify fundamentally distinct disorder landscapes: VG operates in a strongly localized, edge-dominated regime, while NCG forms a continuous percolative network with greater transport stability. Surface functionalization via PASE and amine-terminated ssDNA probes, followed by DNA hybridization across four nucleobase systems, demonstrates that the sequence-dependent electrical response is mechanistically interpretable within the VRH–transconductance framework. NCG transduces biomolecular binding through direct charge transfer and hopping pathway perturbation, whereas VG responds through interfacial electrostatic reorganization. These results introduce a unified VRH–transconductance–sensing framework, providing a rational physical basis for next-generation graphene BioFET design. Full article

With the rapid development of clean and low-carbon energy systems, non-metallic pipelines have become increasingly important in urban gas distribution, water supply, and emerging energy-transport applications, including hydrogen service. As a critical joining technology that governs system integrity and long-term operational safety, electrofusion welding requires a comprehensive and mechanism-oriented understanding beyond empirical process control. In this study, a review is conducted on research published over the past decade in the field of electrofusion welding of non-metallic pipelines, with emphasis on fundamental technical issues including the formation and evolution of temperature fields, characteristics of the molten fusion zone and defect development, and thermo-mechanical coupling with residual stress generation. Based on a synthesis of the literature, the review clarifies the global research landscape, core research communities, and underlying knowledge structure. The results indicate a clear transition of the field from empirically driven parameter optimization toward a mechanism-based and process-controllable paradigm centered on temperature field evolution, fusion zone development, and thermo-mechanical behavior. Current research hotspots converge on HDPE material adaptability, welding process regulation, and the long-term reliability of welded joints. Building on these insights, future research directions are discussed, including mechanism-driven process design, intelligent defect identification based on multi-source data, and full-life reliability assessment under service conditions. This review provides a theoretical framework to support process optimization and engineering application of electrofusion welding in non-metallic pipeline systems. Full article

Vegetation phenology refers to the cyclical growth patterns of vegetation in nature, which are influenced by climatic conditions, human activities, and genetic factors. It plays an irreplaceable role in regulating carbon cycling and energy flow within natural ecosystems. However, the combination of a cloudy and rainy climate with a landscape characterized by the interplay of land and water and fragmented patches has long posed challenges for remote sensing phenological monitoring data, including a scarcity of valid observations, frequent temporal gaps, and spectral distortion in mixed pixels. These issues make it difficult to reliably support the needs of wetland phenological inversion and mapping. To address this issue, this study uses vegetation inversion in the Poyang Lake wetlands as a case study and reconstructs high-spatiotemporal-resolution time-series kNDVI data based on multi-source remote sensing data. Methodologically, we propose an improved and enhanced spatiotemporal adaptive reflectance fusion model, IESTARFM. This model enhances the homogeneity of similar pixel selection through adaptive matching windows and land cover constraints. Additionally, it explicitly incorporates cloud probability and time-lag factors into the weighting structure to systematically downweight unreliable observations, and further employs quadratic term corrections to account for the nonlinear growth response of kNDVI. Using the reconstructed dataset, key phenological information is extracted by combining third-order harmonic analysis with a dynamic thresholding method, thereby enhancing the robust characterization of seasonal trajectories under conditions of missing data and noise. Accuracy evaluation results show that the 10m/8d high-frequency kNDVI dataset reconstructed by IESTARFM achieves at least a 12.61% improvement in fusion accuracy compared to classical methods such as ESTARFM, STARFM, and FSDAF, with a maximum reduction in RMSE of 0.026, and effectively restores details in areas with thin cloud cover. The reconstructed kNDVI series achieved a coefficient of determination $R^2$ = 0.875 and RMSE = 0.066 relative to Sentinel-2 observations, indicating that the reconstructed series closely reproduces the reference imagery in both amplitude and spatial structure. The phenological parameters derived from kNDVI exhibit an RMSE of 4.81 days compared to field observations, demonstrating that the reconstructed time series reliably captures the timing of key phenological events. It should be noted that the proposed approach is designed for post-event time-series reconstruction and is not intended for real-time forecasting. In summary, this study collaboratively enhanced the reliability of high-resolution index time-series reconstruction and phenological identification in cloudy and rainy wetlands through three key aspects: cloud noise suppression, heterogeneous boundary preservation, and nonlinear growth characterization. It provides a generalizable technical foundation for dynamic monitoring of wetland vegetation, ecological restoration assessment, and refined management in regions with frequent cloud and rainfall. Full article

Quantitative structure–activity relationship (QSAR) modeling has conventionally relied on expert-designed molecular descriptors to encode chemical structures. DeepSnap is a descriptor-free QSAR approach that converts prepared three-dimensional molecular conformers into image representations and feeds them directly into convolutional neural networks for activity prediction. This focused narrative review traces DeepSnap from its introduction in 2018 to its current state and places it within the broader landscape of descriptor-based QSAR, topology-based and 3D-aware graph neural networks, and related image-based or semi-image-based molecular representation approaches. Previous studies applied DeepSnap to Tox21 nuclear receptor and molecular initiating event endpoints, rat hepatic clearance, blood–brain barrier penetration, acute oral toxicity, and cosmetics–pharmaceutical compound classification. Across the DeepSnap series, image-based and descriptor-based predictions have provided complementary information, particularly in ensemble or consensus models. However, high or near-ceiling ROC–AUC values reported for selected endpoints should not be interpreted as indicating deterministic or universally generalizable predictions; rather, they should be considered in the context of endpoint-specific model development, image-rendering parameter optimization, possible class imbalance, split dependence, limited matched external replication, and incomplete benchmarking against modern molecular representation models. Limitations include a dependence on nonphysical rendering parameters, single- or representative-conformer input, incomplete matched benchmarking against 2D and 3D molecular representation models, and an interpretability gap addressed in part by CAM-family visualization in the AI-based Substance Hazard Integrated Prediction System (AI-SHIPS) and S-COPHY (a model developed by Shiseido for cosmetics–pharmaceutical compound classification). Future directions include standardized image-generation protocols, conformer-ensemble extensions, systematic interpretability analysis, matched benchmarking, and potential integration with graph-based and 3D-aware molecular learning approaches. Full article

The rapid advancement of AI-driven generative design brings new opportunities, but its application in landscape garden design remains limited by two gaps: (1) semantic misalignment between generated images and the designer’s intent, and (2) low-resolution outputs with insufficient details. To address these gaps, we developed GardenDiff, a domain-adapted diffusion model trained via parameter optimization and a specialized landscape garden dataset. Central to this approach is Structured Design Captioning (SDC), a hierarchical annotation system specifically designed for garden design that encodes design elements, style features, and auxiliary scene information. To develop this model, we designed a three-stage experimental framework. In Stage 1, we examined the effects of training caption systems and training resolution on generated landscape garden imagery by controlled experiments. In Stage 2, we conducted joint training across five garden styles (Chinese, Japanese, Mediterranean, Nordic, and English) based on the optimized parameter settings from Stage 1 to construct the GardenDiff model. In Stage 3, we validated the model performance through expert evaluation (N = 36) and public evaluation (N = 136) and analyzed style-specific variations in the generated outcomes. Research results showed that Structured Design Captioning (SDC) improved Spatial Rationale by 19.67–39.46% compared with generic captions, and training at 1536 × 1536 pixels improved image quality by 23.2% compared with 768 × 768-pixel training. GardenDiff trained with these optimized parameters showed notable advantages. Its overall scores (5.06) exceeded those of Stable Diffusion XL base 1.0 (SDXL 1.0) by 16.4% and DreamShaper XL by 22.4%. The model improved across four dimensions, including Design Rationale, Design Professionalism, Design Accuracy, and Design Satisfaction. Our study offers a new model to improve the perspective visualization of generative garden design and provides insights into AI-informed landscape and urban design. Full article

Background/Objectives: Chronic urate nephropathy (CUN), also referred to as gouty nephropathy, represents a severe renal disease primarily precipitated by long-term hyperuricemia (HUA) and gout. However, the precise molecular mechanisms underlying its pathogenesis remain poorly understood. The present study was designed to explore these mechanisms from the perspective of targeted metabolomics. Methods: The HUA mice constructed by urate oxidase (Uox) gene knockout (KO) and their corresponding wild-type controls were employed for the present study. Serum clinical biochemical parameters were determined, and renal histopathological changes were evaluated using hematoxylin-eosin (HE) staining and Masson’s trichrome staining. A targeted metabolomic strategy based on multiple reaction monitoring (MRM) was utilized to profile the renal metabolic landscape of Uox-KO mice, and potential metabolic biomarkers for CUN were identified via multivariate data analysis. Results: Clinical biochemical analysis revealed a significant elevation in serum uric acid, creatinine, and urea nitrogen levels in Uox-KO mice compared with control mice. Histopathological observations confirmed a typical CUN phenotype in Uox-KO mice, characterized by renal tubular vacuolar degeneration and dilatation, desquamation of tubular epithelial cells into the lumen, neutrophil infiltration, glomerular crowding, and renal interstitial fibrosis. Metabolomic analysis identified a total of 291 differentially regulated metabolites in Uox-KO mice relative to control animals. These perturbed metabolites were involved in multiple key biochemical pathways, including amino acid biosynthesis, ABC transporter signaling pathway, purine metabolism, aminoacyl-tRNA biosynthesis, protein digestion and absorption, glycerophospholipid metabolism, and serotonergic synaptic transmission. Notably, pathological parameters, including biochemical measurements and histological observations, were significantly correlated with key differential metabolites associated with CUN progression. Furthermore, eleven differential metabolites (pyroglutamic acid, fructose, riboflavin, dimethyl-L-arginine, glucaric acid, indoxyl sulfate, palmitoylethanolamide, trimethylamine N-oxide, 3-hydroxyanthranilic acid, spermidine, and hippuric acid) were identified as potential metabolic biomarkers for the diagnosis and prognosis of CUN. Conclusions: These findings illustrate that targeted tissue metabolomic analysis constitutes a powerful tool for deciphering the molecular mechanisms of diseases, thus offering novel insights into the pathogenesis of CUN. Full article

Urban design that improves microclimate can significantly enhance the ecological livability of human settlements, while the climate-adaptive wisdom of applying local water-net landscapes to modern urban renewal requires further validation. To investigate the optimization mechanism of waterscape on microclimate comfort, this study focuses on the public space of Xinsheng District in the Suzhou water-net region. By integrating continuous incremental multi-scenario form design, computational fluid dynamics (CFD) multi-physics simulation, and climate sensation evaluation, we reproduce the spatial differentiation of microclimate and comfort gradients across multi-hour periods during hot summer daytime within the built-up environment involving waterbodies, vegetation, and buildings. Consequently, an indicator of comfort improvement efficiency (CIE) is proposed to measure the spatial effectiveness of per-unit-area water surface expansion on climate sensation. Results show that when controlling other morphological parameters and designing three incremental waterbody scenarios—no water surface, 50% water, and 100% waterscape—the relative comfort area expanded across all time periods as water increased. This implies that waterscape variations exert a positive effect on microclimate suitability. However, during the expansion of water area at each time, the CIE was higher in the 0–50% initial stage of water surface increase compared to the 50–100% later morphological stage. Therefore, this study reveals the stepwise nonlinear trend by which increased water area in the built-up environment improves the climate suitability of waterfront spaces. Furthermore, under constraints of equivalent area and other geometric forms, a more dispersed and networked waterscape was found to be a superior spatial strategy. This confirms the microclimate wisdom of the water-net landscape in the Jiangnan locality, providing form optimization guidance for ecologically oriented urban renewal design. Full article

Although livestock buildings constitute a widespread and structurally significant component of the rural landscape, they are, in most cases, characterised by construction configurations primarily driven by production requirements. Such an approach rarely results from a conscious design process capable of integrating architectural criteria with the environmental context in which these structures are embedded. Within this framework, the prevailing construction model—based on prefabricated steel systems and sandwich panels—prioritises rapid execution, standardisation, and cost efficiency, while relegating aspects such as environmental quality, material circularity, and landscape integration to a marginal role. Against this background, the present study investigates the possibility of redefining this paradigm through a technological substitution grounded in the principles of bio-based construction, technological design, and circular economy. To this end, a timber-based architectural solution for poultry houses is developed and adopted as an experimental case study to assess environmental and economic performance through an integrated methodology combining Life Cycle Assessment (LCA) and Construction Cost Analysis. The evaluation is conducted comparatively against a conventional steel-based system, maintaining consistent geometric and functional parameters, within the climatic context of the Italian Mediterranean and in accordance with EN 15978 and EN 15804+A2 standards, over a 30-year reference period. The results indicate a significant reduction in environmental impacts for the timber-based solution, with a decrease in Global Warming Potential of approximately 29%, reaching values close to 50% when accounting for biogenic carbon storage. From an economic perspective, the alternative solution entails an increase in initial costs of approximately 20%, primarily associated with the adoption of a high-performance building envelope. Overall, the study demonstrates how architectural technological design, when supported by quantitative assessment tools, can operate as an effective driver for the ecological transition of rural productive landscapes. Full article

Soil salinization is a major abiotic stressor limiting global agricultural and forestry productivity. This study aimed to assess the tolerance of four wild cherry (Prunus avium L.) genotypes (8-A, F-12, F-19, F-15) to salinity stress using the in vitro culture technique. Shoots were exposed to three NaCl concentrations (0—control treatment, 33, and 100 mM) in micropropagation medium under controlled laboratory conditions for 35 days. Morphological parameters, including shoot length, shoot number, survival and multiplication rate, shoot fresh and dry biomass, and shoot water content, were evaluated alongside biochemical markers such as total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activities assessed through ferric reducing–antioxidant power (FRAP), ABTS radical scavenging, DPPH radical scavenging and nitric oxide (NO•) scavenging. Consistent with the experimental design, exposure to 100 mM NaCl significantly inhibited shoot growth and biomass accumulation, while survival was comparatively less affected. Genotypic variation was evident, with genotypes F-19 and F-12 demonstrating higher tolerance, maintaining greater growth and antioxidant capacity (FRAP and ABTS) under salt stress compared to more sensitive genotypes like 8-A and F-15. Phenolic and flavonoid contents were also reduced at 100 mM NaCl, suggesting that intense salinity stress limited the biosynthesis and accumulation of these antioxidant compounds. Nitric oxide scavenging activity remained largely unaffected by salinity in all genotypes, which may indicate that the applied stress levels were insufficient to markedly alter this component of the antioxidant response. The genotype F-19 emerged as the strongest salinity-tolerant genotype, retaining superior shoot number, multiplication rate, fresh/dry biomass and stable/increased total phenolic content (TPC) under 100 mM NaCl compared to other genotypes. This integrative in vitro approach effectively distinguished salt-tolerant wild cherry genotypes and offers a valuable screening tool for breeding and selection programmes targeting improved resilience to salinity stress. The findings have practical relevance for forestry, horticulture, landscape architecture and the restoration of salt-affected sites, particularly in the context of climate change. They also align with current European and global priorities focused on identifying genetically suitable reproductive material for resilient afforestation and ecosystem restoration under increased environmental stress. Full article

This study introduces a novel Quantum-Inspired Hybrid Bald Eagle-Ukari Algorithm with Reinforcement Learning (QI-HBEUA-RL) for comprehensive optimization of conical solar distillers equipped with sand-filled copper conical fins. The proposed algorithm synergistically combines quantum computing principles (superposition and entanglement), bio-inspired metaheuristics (Bald Eagle Search and Ukari Algorithm), and reinforcement learning mechanisms to achieve unprecedented optimization performance in complex thermal-hydraulic systems. The QI-HBEUA-RL framework employs quantum-encoded population representation, enabling simultaneous exploration of multiple solution states, while reinforcement learning dynamically adjusts algorithmic parameters based on search landscape characteristics and historical performance data. Experimental validation tested seven distiller configurations in El-Oued, Algeria, under controlled conditions (7.85 kWh/m²/day solar radiation, 42.2 °C ambient temperature). The optimal configuration of copper conical fins with 14 g sand at 0 cm spacing achieved: daily productivity of 7.75 L/m²/day (+61.46% improvement over conventional design), thermal efficiency of 61.9%, exergy efficiency of 4.02%, and economic payback period of 5.8 days. Comprehensive algorithm comparison against six state-of-the-art multi-objective optimizers (NSGA-II, MOEA/D, MOPSO, MOGWO, MOHHO) across 30 independent runs demonstrated statistically significant superiority (p < 0.001, Wilcoxon test). QI-HBEUA-RL achieved 7.42% improvement in hypervolume indicator, 29.35% reduction in inverted generational distance, and 19.49% better solution spacing. Generalization validation on seven benchmark problems (ZDT1-6, DTLZ2, DTLZ7) and three renewable energy applications confirmed algorithm robustness across diverse problem types. Three real-world case studies, remote village water supply (238:1 benefit–cost), industrial facility (100% energy reduction), and emergency relief (740× cost savings) validate practical implementation viability. This research advances solar thermal desalination technology and multi-objective optimization methodologies, providing validated solutions for sustainable freshwater production in water-scarce regions. Full article

Achieving high selectivity in the hydrogenation of CO₂ to light olefins remains challenging because of the complex reaction network and the difficulty of regulating key intermediates. Motivated by green-hydrogen-enabled power-to-chemicals pathways, we combine density functional theory (DFT) with first-principles microkinetic simulation (FPMS) to construct a quantitatively predictive reaction-energy landscape and elucidate structure–selectivity relationships. A comprehensive reaction network is established through energy-surface fitting, and steady-state rate constants are solved to capture the microkinetic competition between elementary steps. By introducing electronic density-of-states (DOS) modulation as a design variable, we directly correlate surface structural parameters with rate-controlling steps, thereby enabling targeted regulation of C–C coupling and hydrogen transfer processes. The calculated barrier for CO₂ adsorption to COOH* is 1.35 eV, while the transition state barrier for C–C coupling is 1.50 eV, corresponding to a reaction rate of 9.7 × 10³ s⁻¹; the olefin desorption rate reaches 1.7 × 10⁷ s⁻¹. Crucially, shifting the d-band center from −2.35 eV to −1.60 eV increases the C₂–C₄ olefin selectivity from 42.6% to 68.3%, establishing an actionable electronic structure lever for catalyst optimization. These results reveal the intrinsic mechanism by which surface electronic and geometric regulation governs intermediate stabilization and rate control, providing a verifiable, mechanism-based design principle for efficient CO₂-to-olefin catalysts aligned with green hydrogen deployment. Full article

Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) efficiency constrains carbon fixation, making it a high-value target in biotechnology. The core task of this work is a supervised regression and ranking problem on Rubisco: given a numerical representation of a protein sequence (a PLM embedding), we predict continuous phenotypic scores such as an enzyme kinetic proxy or fitness value. The predictions then guide which variants to test next. Engineering Rubisco is a point of focus but remains challenging due to selection forces in vivo and the combinatorial space of potential mutants for ex vivo uses. We combine protein language model (PLM) embeddings with tabular learning to model Rubisco variant landscapes in two regimes. First, we analyze deep mutational scanning data providing inferred kinetic proxies, including K_m for CO₂ and V_max. Second, we model a cyanobacterial screening dataset measuring mutant fitness under differing oxygen and nitrogen regimes, enabling an oxygen tolerance objective. Across tasks, a tabular foundation (TabPFN-2.5) model outperforms gradient-boosted trees on rank-based criteria for variant prioritization, including Spearman correlation and top 5% hit recovery. We then simulate active-learning campaigns initialized with 200 measured variants and iteratively acquiring batches of 48. Model-guided selection recovers more top-performing mutants than random sampling at fixed experimental budgets, even with a conservative XGBoost surrogate. We also demonstrate that Rubisco large-subunit embeddings predict cyanobacterial doubling time and cross-species kinetic parameters, suggesting that Rubisco representation remains meaningful across organisms even with multi-objective cellular constraints. Together, these results support a practical, data-efficient workflow for enzyme engineering and motivate objective-aware design strategies that complement directed evolution. Full article

Non-convex stochastic optimization presents fundamental mathematical challenges across machine learning, wireless networks, data center resource allocation, and optical wireless communication systems, where complex loss landscapes with multiple local minima and saddle points impede classical variational inference methods. This paper introduces the Quantum-Inspired Variational Inference (QIVI) framework, which systematically integrates quantum mechanical principles (superposition, entanglement, and measurement operators) into classical variational inference through rigorous mathematical formulations grounded in Hilbert space theory and operator algebras. We develop a unified optimization framework that encodes classical parameters as quantum-inspired states within finite-dimensional complex Hilbert spaces, employing unitary evolution operators and adaptive basis selection governed by gradient covariance eigendecomposition. The core mathematical contribution establishes that QIVI achieves a convergence rate of $\mathit{O}({log}^{2}T/T^{1/2})$ for $\sigma$-strongly non-convex functions, provably improving upon the classical $\mathit{O}\left(T^{-1/4}\right)$ rate, yielding a theoretical speedup factor of $1.85$–$1.96\times$. Comprehensive experiments across synthetic benchmarks, Bayesian neural networks, and real-world applications in network optimization and financial portfolio management demonstrate 23–$47\%$ faster convergence, 15–$35\%$ superior objective values, and 28–$46\%$ improved uncertainty calibration. The principal contributions include: (i) a rigorous Hilbert space-based mathematical framework for quantum-inspired variational inference grounded in operator algebras, (ii) a novel hybrid quantum–classical algorithm (QIVI) with adaptive basis selection via gradient covariance eigendecomposition, (iii) formal convergence proofs establishing provable improvement over classical methods, (iv) comprehensive empirical validation across diverse problem domains relevant to machine learning and network optimization, and (v) demonstration of the framework’s applicability to optimization problems arising in wireless networks, data center resource allocation, and network system design. Statistical validation using the Friedman test (${\chi }^2=847.3$, $p<0.001$) and post hoc Wilcoxon signed-rank tests with Holm–Bonferroni correction confirm that QIVI’s improvements over all baseline methods are statistically significant at the $\alpha =0.05$ level across all benchmark categories. The framework discovers $18.1$ out of 20 true modes in multimodal distributions versus $9.1$ for classical methods, demonstrating the potential of quantum-inspired optimization approaches for challenging stochastic problems arising in machine learning, wireless communication, and network optimization. Full article

Online ride-hailing platforms increasingly rely on differentiated incentive mechanisms to regulate driver participation and balance supply and demand. However, drivers’ adaptive responses to such incentives introduce dynamic feedback and uncertainty that static equilibrium models fail to capture. This study develops a dual-layer Stackelberg–evolutionary game framework in which the platform acts as a strategic leader setting the order allocation rates and prices, while heterogeneous drivers adapt their working-hour strategies through evolutionary dynamics. Using operational data from Ningbo, China, we calibrated the demand elasticity and driver cost parameters and identified endogenous fatigue-cost thresholds that govern regime shifts in strategy dominance. Simulation results show that uniform incentives tend to drive the system toward single-strategy lock-in, whereas differentiated order allocation and pricing effectively sustain multi-strategy coexistence and mitigate extreme supply polarization. The findings reveal how platform-led differentiation reshapes the evolutionary fitness landscape of drivers, providing actionable guidance for incentive design aimed at stabilizing supply structures, improving platform revenue, and protecting driver welfare. Full article

This research is interdisciplinary in nature and supports the process of selecting individual plants to achieve sustainable visual and ecological effects in the urban landscape. The importance of this study is further emphasised by climate change, which necessitates modifications to the existing selection of ornamental plants. These individuals must be capable of adapting to urban ecosystems in order to mitigate the impacts of climate change on humans and other organisms and to maintain a high level of biodiversity. Accordingly, this paper highlights, at the individual level, the significance of Japanese quince (Chaenomeles japonica (Thunb.) Lindl. ex Spach) as an element of urban green infrastructure in the Balkan Peninsula. Based on a real case study conducted over the period 2007–2025 and through an integrative approach involving 3841 phenological observations and climate parameters over 19 consecutive years, local phenological flowering patterns were identified, upon which the species’ functional potential depends. The key patterns and abundance of flowering are the result of interactions with daily maximum and minimum air temperatures and precipitation levels, as confirmed by correlations with percentile-based classifications of climatic variables for the study years. The statistical non-significance of the trends points to the influence of extreme climatic events but also to the adaptability of the selected genotype compared with other Japanese quince genotypes in the vicinity. Regression analysis determined the optimal daily air temperatures for continuous flowering during 2024 and 2025. The results confirm that the selected individual is sustainable, and it is, therefore, proposed for inclusion in the assortment of ornamental plants important for preserving ecosystem services in urban landscape design, particularly in view of its demonstrated utilitarian benefits. Full article