Search Results (1,990)

Faba bean yield reflects complex relationships among genotype, environment, and agronomic traits. This study evaluated an explainable Tabular Prior-data Fitted Network (TabPFN) framework for estimating plot-mean single-plant yield and prioritizing traits using 398 plot-level observations, 13 measured agronomic predictors, and six derived features. On the reference 80/20 split, TabPFN achieved the best values for all four test metrics (R² = 0.8746, RMSE = 1.9132 g plant⁻¹, MAE = 1.0819 g plant⁻¹, and MAPE = 8.16%). The Friedman test detected differences among the six models (χ²(5) = 16.75, p = 0.005); Nemenyi comparisons distinguished TabPFN from HistGradientBoosting and SVR, whereas the Holm-corrected Wilcoxon analysis confirmed only the TabPFN–SVR difference. Across 10 repeated 80/20 splits, TabPFN obtained the highest mean test R² (0.8614 ± 0.0691), ranked first in eight splits, and produced a higher R² than every tuned baseline in at least eight splits. SHAP, permutation importance, and LOCO analyses emphasized pod-, seed-, and biomass-related predictors. Repeated-split ablation showed that derived features improved TabPFN consistently, whereas removing selected target-proximal yield variables reduced performance for every model. The framework is therefore a harvest-time trait-estimation and trait-prioritization tool rather than an early-season forecasting system. Notably, TabPFN achieved this performance without the 100-trial Optuna search used for each baseline; only n_estimators was screened over four prespecified values. Full article

Pulse-processing plants generate large volumes of screenings, that is, undersized, broken and foreign-matter-contaminated grain that is removed before the material enters the human food chain. Their feeding value for ruminants is still poorly documented. This study characterised the chemical composition, Cornell Net Carbohydrate and Protein System (CNCPS) nitrogen fractions, in vitro ruminal dry matter degradability (IVDMD) and rumen fermentation behaviour of screenings from five pulse species: soybean, dry bean, chickpea, green lentil and red lentil. Six independent batches per species (n = 30) were obtained from processing plants in different regions of Türkiye. Total digestible nutrients (TDNs) and energy values were estimated with NRC (2001) equations, gas production was recorded for 48 h in a semi-automatic modular system and fitted to a logistic model, IVDMD was measured in a Daisy^II incubator and volatile fatty acids (VFAs), pH and NH₃-N were determined after 24 h of incubation. Differences were declared at p < 0.05 throughout. Soybean screenings had the highest crude protein (35.99%), recalculated TDNs (75.26%) and 48 h gas production (65.16 mL per 460 mg of incubated sample), a ranking that largely disappears once gas is expressed per gram of incubated organic matter; chickpea screenings combined the highest starch (50.67%), IVDMD (71.27%) and fractional degradation rate. Dry bean screenings fermented most slowly, with a lag time of 10.46 h. Acetate, propionate and total VFAs did not differ among species. No parent grain was analysed alongside the screenings, so any comparison with clean pulse grain rests on published values for other samples and cultivars. Within that limit, the screenings carry enough protein, calculated energy and rumen-undegradable protein to warrant testing as partial replacements for conventional concentrates in feeding trials. Full article

Exogenous application of bio-based nanomaterials provides a targeted strategy to modulate plant physiological and biochemical responses. This review synthesizes recent advancements in the foliar application of nanocellulose (NC), in particular, cellulose nanocrystals (CNC) and cellulose nanofibers (CNF), to enhance plant fitness. CNC-formed films provide physical and biochemical barriers that increase plant drought and cold stress tolerance. Topically applied CNC reduce non-stomatal transpiration and serve as insulators, allowing the flowering buds to successfully survive chilling, avoid freezing, and maintain cell membrane integrity. Simultaneously, CNC- and CNF-formed coatings are porous enough not to block the natural gas exchange essential for plants. CNC trigger internal antioxidant defense systems, upregulating reactive oxygen species-scavenging enzymes and modulating molecular signaling cascades. NC foliar treatment suppresses the growth of pathogenic bacteria and fungi, interferes with their adhesion and plant tissue penetration, and prevents biofilm formation. Thus, topical NC application could be regarded as a multi-functional tool for precision crop management and protection. Full article

Sustainable management of biodegradable waste is a key component of the circular economy and resource recovery strategies. Composting is a complex biological process whose efficiency depends on numerous operational and physicochemical factors. Under industrial conditions, continuous laboratory monitoring of waste properties is often limited by time and cost constraints, necessitating reliable predictive tools to support process management. This study investigates the use of artificial neural networks (ANNs), decision trees (C&RT), and principal component analysis (PCA) for optimizing the composting of biodegradable waste under industrial-scale conditions. The research was conducted at a full-scale mechanical–biological treatment facility in Poland processing both the organic fraction mechanically derived from mixed municipal waste and separately collected biowaste. A dataset containing 23 records was developed from operational parameters (airflow, water addition, turning frequency, and process duration) and physicochemical properties of composted waste, including moisture content (MC), loss on ignition (LOI), total organic carbon (TOC), respiration activity (AT4), and higher heating value (HHV). The best-performing neural model achieved a predictive accuracy of 0.999 (coefficient of determination R² in the test set). For each of the neural networks, goodness of fit indices were also determined: MAE and RMSE. PCA confirmed strong relationships among key waste properties, while decision tree analysis identified airflow as the dominant operational factor affecting MC, LOI, and TOC, whereas turning frequency had the strongest influence on AT4. The results demonstrate that machine learning tools can effectively support industrial composting optimization by predicting operational parameters required to achieve desired waste stabilization characteristics, providing practical decision-support solutions for composting plant operators. It is recommended to implement single-output MLP models for dynamic, real-time process control and C&RT rules as emergency procedures. This study aligns with circular economy principles and the Sustainable Development Goals by demonstrating the potential of artificial intelligence to enhance sustainable biodegradable waste management, resource recovery, and industrial composting performance. Full article

This study addresses a large-scale location–allocation problem in a three-echelon automotive supply chain comprising 382 suppliers, candidate distribution centers, and six assembly plants. The planning task is to redesign the inbound consolidation network while minimizing transportation and distribution center operating costs, enforcing a 480 km supplier-to-center service radius, and achieving at least 90% demand-weighted coverage. We formulate a mixed discrete-continuous model with supplier-to-center assignment, center location, throughput, and flow decisions. A feasibility-oriented hybrid algorithm uses a genetic algorithm as the main search engine, ant colony construction to seed solutions near the feasible region, adaptive mutation and simulated annealing to preserve exploration and refine elite solutions, and an online neural surrogate to avoid a subset of costly exact fitness evaluations. The design differs from a simple collection of metaheuristics: all components share one variable-length encoding, the same feasibility metrics, and periodic exact reevaluation of candidate solutions. Using the competition case data, the redesigned network reduces total cost by 27.0% relative to the six-center baseline, decreases the demand-weighted average supplier-to-center distance from 461.3 km to 53.0 km, lowers the maximum distance from 2807.22 km to 441.78 km, and raises coverage from 45.0% to 100%. Across ten independent runs, the hybrid method obtains a mean cost 10.3% below that of a standard genetic algorithm, with lower run-to-run dispersion. The results show that feasibility-aware initialization, adaptive search, and selective surrogate evaluation can support practical redesign of a strongly constrained, national-scale inbound logistics network. The directly attached reproducibility package provides the MATLAB implementation and the seven supplied input workbooks used by the reported model. The evidence is limited to one deterministic competition instance, a fixed cost schedule, and fixed-topology sensitivity calculations; generalization under demand uncertainty, facility disruption, and alternative road conditions remains to be tested. Full article

Urban streets are important heat-exposure environments, yet the relationships of two-dimensional (2D) planar plant morphology and three-dimensional (3D) vegetation structure with land surface temperature (LST) remain insufficiently integrated at a continuous street scale. We analyzed 42,603 street-scale study units in the central urban area of Wuhan, defined at 50 m sampling intervals with a 150 m radius. The 2D variables comprised green-space area (A), mean patch perimeter (P_mean), and perimeter–area ratio (P_A), while the 3D variables comprised green view index (GVI), mean 3D green volume (NV_mean), mean canopy height (CH_mean), and canopy-height variability (CH_sd). Anselin Local Moran’s I identified High–High (HH) and Low–Low (LL) zones; linear regression (LR), random forest (RF), and SHAP characterized linear, nonlinear, and model-based contributions; and buffered spatial cross-validation and spatial resampling evaluated robustness. Under the original random 80–20% train–test split, LR/RF R² values were 0.2153/0.4002 for the overall study area, 0.1940/0.3539 for the HH zone, and 0.0488/0.4853 for the LL zone. Under five-fold buffered spatial cross-validation, the corresponding pooled out-of-fold R² values were 0.1940/0.2237, 0.1427/0.0965, and −0.0942/−0.0231, showing that the RF advantage weakened after spatial separation and did not persist in the HH and LL zones. In the original fitted RF models, A, P_mean, and NV_mean had the largest mean absolute SHAP contributions overall; A, P_A, and P_mean ranked highest in the HH zone; and GVI, CH_sd, and CH_mean ranked highest in the LL zone. Repeated buffered spatial validation showed no stable overall 2D predominance because the median 2D share of 52.1% had a 46.5–58.8% percentile range, but it supported stable 2D relative predominance in the HH zone (61.0% [55.5–66.7%]) and stable grouped 3D relative predominance in the LL zone (61.9% [52.3–70.3%]), despite unstable LL variable-level rankings. SHAP relationships were nonlinear and zone-dependent: A and P_mean showed clearer directional transitions overall and in the HH zone, whereas the LL zone and most 3D variables exhibited multiple directional changes. Spatial-block bootstrap analysis examined 40 full-sample zero-crossing candidates, of which 39 met the predefined stability criteria; these ranges represent model-derived directional transitions rather than ecological thresholds or causal planning standards. The findings demonstrate thermal-context-dependent, model-based associations between 2D and 3D plant morphology and street-scale LST, while emphasizing that model performance and some importance rankings are spatially sensitive and require local validation. Full article

Accurate prediction of the state trend of pumped storage units (PSUs) is essential for timely anomaly detection and preventive maintenance to improve the overall economic performance of power plants. Nevertheless, the complex and time-varying characteristics of PSU vibration data increase the difficulty of accurately modeling their dynamic evolution. In response to this problem, an integrated vibration trend prediction (VTP) method for PSUs is developed by combining a temporal-enhanced generative adversarial network (TEGAN) with an improved bidirectional long short-term memory network (IBiLSTM). Firstly, TEGAN expands the original dataset by synthesizing artificial samples, thereby improving the structural diversity and representativeness of vibration data. Within TEGAN, a temporal characterization (TC) module is designed to collaboratively guide the generator and the discriminator, while a data processing module is adopted to incorporate structural priors into the learning process. Secondly, variational mode decomposition (VMD) is applied to decompose the original vibration data into intrinsic modes, followed by PSR to reconstruct the components of each modality into a higher-dimensional state space representation. Subsequently, by incorporating the proposed multi-order Kolmogorov–Arnold network (M-KAN) for high-order nonlinear fitting, IBiLSTM is employed to model each reconstructed sub-sequence. Finally, the outputs of all sub-sequences are aggregated to produce the final VTP results. The comparative evaluation verifies the advantages of the developed method in terms of prediction accuracy and robustness for PSU vibration trend forecasting. Full article

Twin Rotor Multi-Input Multi-Output (TRMS) platforms combine strong aerodynamic cross-coupling, gravitational loading on the vertical axis, friction-dominated horizontal dynamics, and systematic mismatch between idealised models and laboratory hardware. The platform provides only two optical encoders, so the angular rates and the rotor states are unavailable for feedback. This paper presents an observer-based output-feedback architecture that addresses both difficulties. A high-gain observer built on the fully coupled six-state model, including the gyroscopic terms that the control design cannot retain, reconstructs the four unmeasured states from the two encoder angles. The reconstructed states drive a Hybrid Backstepping–Super-Twisting (B-STA) controller in which a second-order continuous sliding mode is embedded at the final recursive step through a composite surface. Because backstepping requires strict-feedback structure, which the centralised coupled model does not possess, the controller is synthesised on a decentralised design model and the residual coupling is rejected as a matched perturbation of the sliding variable. Closed-loop behaviour is analysed as a three-stage cascade covering observer error, sliding variable, and tracking error, yielding practical stability under bounded residuals with an explicit input-to-state gain. The residual bounds are evaluated numerically from the actuator saturation limit and the identified coefficients rather than assumed, and the resulting figures are shown to predict the marked difference in sliding-variable behaviour observed between the two axes. A second architecture applies a windowed Grey Wolf Optimiser (B-GWO) to the backstepping gains, in which each candidate is applied to the plant for a fixed test window, scored on its own accumulated integral of time-weighted absolute error, and followed by a settle window at the incumbent best. We prove that this windowing is a requirement rather than a convenience: a fitness evaluated at a single sample is common to all candidates, cancels from the population ranking, and reduces the search to the minimiser of its own regularisation term. Both schemes are implemented on a physical TRMS through a Simulink Desktop Real-Time interface at a control period of $10\mathrm{ms}$. On a $100\mathrm{s}$ experimental run, B-STA attains a pitch tracking error of $0.0318\mathrm{rad}$ RMS, $7.94\%$ of the reference amplitude, and the lowest control energy on both axes among the strategies compared, reducing pitch control energy by $72.9\%$ relative to a first-order Backstepping–Sliding Mode baseline recorded on the same interface. Numerical disturbance rejection tests on the fully coupled model confirm the mechanism: under a matched actuator step the super-twisting integrator state migrates to a new steady level that cancels the disturbance, driving the residual pitch error to $2\times {10}^{-4}\mathrm{rad}$, whereas the same recursive law without the second-order injection retains a permanent offset of $0.28\mathrm{rad}$. Full article

Nonlinear mixed-effects models (NLMMs) provide a flexible framework for modeling repeated-measures trajectories. However, how best to forecast future observations, especially at ages well beyond those represented in the data, remains relatively underexamined. In this study, we develop a Chapman–Richards NLMM with a spatial-power autocorrelation structure for irregularly spaced repeated measures and compare three forecasting strategies: (i) population-averaged forecasts based on the fixed-effects component only; (ii) subject-specific forecasts in which empirical best linear unbiased predictors (EBLUPs) of the random effects are obtained via a first-order Taylor series expansion with an iterative Newton–Raphson update, including the case of new progenies not used in model fitting; and (iii) forecasts that combine the population-averaged prediction with prior repeated measures through the fitted autocorrelation matrix. Forecast accuracy was assessed with progeny-level validation under fully held-out and partially observed scenarios, using root mean square prediction error (RMSPE) and mean absolute error (MAE), and was examined as a function of: (i) the number of available prior measures and (ii) the accuracy of the fixed-effects component of the NLMM. The methods were illustrated with repeated-measures data from hybrid spruce (Picea engelmannii Parry ex Engelmann × Picea glauca (Moench) Voss) progeny trials at three planting sites in British Columbia, Canada, with measurement ages from 2 to 42 years. Subject-specific forecasts had the lowest prediction errors when sufficient prior measures were available and were also the least affected by misspecification of the fixed-effects component. With only two prior measurements, autocorrelation-based forecasts had the lowest or tied-lowest observed errors, although differences among the three approaches were small. Using all measurements taken before age 42, subject-specific forecasts of height at age 42 achieved an RMSPE of 0.50 m. With only two prior measurements, the corresponding RMSPEs were approximately 1.31–1.33 m across the forecasting approaches. Although demonstrated with a single hybrid spruce dataset from three planting sites, the comparison is, in principle, applicable to other repeated-measures settings in which long-horizon predictions are required from short observation histories; broader applicability remains to be confirmed. Full article

The rhizosphere microbiome is critical for plant health, yet how soil type and intensive management jointly govern its assembly remain unclear. Here, we hypothesized that soil type acts as a primary environmental filter, while intensive cultivation (plant growth plus fertilization) imposes additional selective pressures that differentially shape bacterial versus fungal communities. Using flue-cured tobacco (K326) grown in clay loam and sandy loam soils under field conditions, we examined the rhizosphere microbiome at the topping stage. Intensive cultivation significantly altered rhizosphere physicochemical properties. Key nutrients, including organic matter (OM), dissolved total nitrogen (DTN), available phosphorus (AP), and available potassium (AK), were markedly enriched. Rhizosphere soil pH exhibited a bidirectional shift relative to the corresponding bulk soil, converging to a narrow range (7.4–7.8) in both soil types. Root activity and fertilization imposed contrasting selective pressures on the two microbial kingdoms: bacterial diversity declined slightly, indicating strong deterministic selection, whereas fungal diversity increased, reflecting adaptation to root-generated niches. Differential abundance analysis identified 38 bacterial OTUs as a core rhizosphere-adapted microbiome shared across both soil types, demonstrating robust fitness in the nutrient-enriched rhizosphere environment under intensive management. No shared core fungal OTUs were detected, underscoring strong soil legacy effects and higher habitat specificity in fungi. Notably, the core bacterial microbiome was dominated by K-strategists (slow-growing, resource-efficient taxa) that exhibited opportunistic traits capable of rapidly exploiting nutrient pulses in the rhizosphere. Together, these findings reveal that soil type acts as a critical filter modulating plant–microbe interactions under intensive agriculture, while bacteria and fungi employ divergent ecological strategies in response to selection pressures. This work provides both theoretical and practical insights for optimizing tobacco cultivation and sustaining soil microecological health. Full article

Recently, plant-based bioelectronic systems have been explored for environmental sensing applications. However, their intrinsically low electrical conductivity often limits signal sensitivity and measurement reliability. In this work, the electrothermal behavior of living Epipremnum aureum plants incorporating Ag/AgCl nanoparticles supported on nanocellulose was investigated. Electrical and thermal responses were simultaneously measured under controlled environmental conditions using external shunt resistances of 1, 10, 100, and 1000 Ω. Compared with the control without nanoparticle incorporation, the nanoparticle-incorporated plant exhibited stronger electrical responses and distinct electrothermal behavior over the studied temperature range. The measured signals showed nonlinear responses, temporal asymmetry, and resistance-dependent modulation, suggesting changes in charge transport within the plant tissues. Silver-enriched regions and the co-detection of chlorine within the nanoparticle-incorporated plant tissues were identified by environmental scanning electron microscopy and energy-dispersive X-ray spectroscopy. Five machine-learning regression models were trained to estimate temperature using the measured electrothermal voltage signals as predictors. The best-performing model, MLP FitRNet, achieved a mean absolute error of 0.598 °C, a root mean square error of 0.748 °C, and an $R^2$ value of 0.974. These results demonstrate the potential of nanoparticle-incorporated biohybrid plant systems for electrothermal signal analysis and data-driven temperature estimation, while providing a foundation for future intelligent environmental monitoring applications. Full article

Aronia melanocarpa contains abundant phenolic compounds, including procyanidins, flavonols, flavan-3-ols, and phenolic acids, but its pronounced astringency limits its broader use in food applications. Catechol and other oxygen-donor motifs in plant phenolics are known to associate with Fe³⁺ and can support metal–phenolic assembly. In this study, ultrasound-assisted cellulase–pectinase extraction was used to recover an A. melanocarpa polyphenol extract, which was subsequently combined with Fe³⁺ to construct a plant-derived hair dye coating on keratin fibers. Under optimized extraction conditions, the total phenolic yield reached 82.315 mg gallic acid equivalents (GAE)/g dry fruit. A limited targeted LC–MS/MS panel was used to evaluate 12 selected non-anthocyanin phenolic analytes, including procyanidin dimers, a flavan-3-ol, flavonols, and phenolic acids; the contribution of the pH-sensitive anthocyanin fraction was not resolved. Among the tested metal ions, Fe³⁺ produced the strongest chromogenic response. The optimized L-cysteine pretreatment–Fe³⁺ mordanting–polyphenol dyeing sequence produced a ΔE* of 55.42. Adsorption experiments empirically described the uptake behavior of the polyphenol extract, whereas complementary surface, spectroscopic, elemental, diffraction, thermal, and wettability analyses were consistent with the presence of a relatively uniform Fe-containing polyphenol coating at the hair fiber interface. Neither the adsorption-model fits nor the individual characterization techniques uniquely resolved the underlying molecular mechanism. Preliminary HET-CAM, single-exposure dermal, and acute eye irritation assessments showed no obvious acute irritation under the tested conditions. An exploratory image-based analysis examined whether standardized hair tress photographs could approximate a predefined colorimetric score. Because only 30 independent original samples were available and no external validation was performed, this analysis was treated solely as a proof of concept. This laboratory proof-of-concept extends established Fe³⁺–polyphenol assembly chemistry to a compositionally complex A. melanocarpa extract for hair fiber coloration. Further work is required to clarify anthocyanin behavior, simplify the sequential protocol, and benchmark its performance against representative commercial hair dyes. Full article

The rising global food demand requires boosting agricultural productivity without compromising environmental sustainability, especially in the face of intensive agrochemical use and soil degradation. Based on this, strain TRQ67 was isolated from wheat rhizosphere soil in the Yaqui Valley, Mexico, and characterized morphologically, biochemically, and genomically. Strain TRQ67 possesses a genome of 4.04 Mbp across 37 contigs with a G + C content of 46.3%, comprising 4127 coding DNA sequences (CDSs), and was identified as Bacillus velezensis through Overall Genome Relatedness Indices (OGRIs), including Average Nucleotide Identity (OrthoANI = 99.12%) and Genome-to-Genome Distance Calculator (Formula 2: 92.6%). The genome revealed key functional genes associated with auxin biosynthesis (trpABCDEF and yhcX), iron acquisition (dhbABF), nutrient solubilization (gabD, acnAB and pyc), stress response (clpCEPX and pspA), antifungal metabolite synthesis (srfAABCD, fenABCD and bmyABC), chemotaxis and motility (cheABCD, motAB, flgBCDEF, swrC), bacterial fitness (acoABR, acuABC and budABC), exopolysaccharide production (epsDEFHI), sporulation (spo0ABEF) and bioremediation. Predicted gene functions were supported by in vitro phenotypic assays; strain TRQ67 was able to solubilize phosphate (Solubilization Index of 4.1 ± 0.46), biosynthesize siderophores (Production Index of 1.70 ± 0.16), and produce indoles (6.52 ± 0.63 µg mL⁻¹). Furthermore, this strain demonstrated antagonistic activity against phytopathogenic fungi Fusarium languescens and Bipolaris sorokiniana, resulting in reductions in fungal growth area of 87.33% and 89.28%, respectively. These antagonistic effects are consistent with the presence of Biosynthetic Gene Clusters (BGCs) encoding lipopeptides (surfactin and fengycin), polyketides (difficidin, bacillaene and macrolactin H), dipeptides (bacilysin) and siderophores (bacillibactin), as identified through antiSMASH analysis. Finally, the strain significantly improved root (27.63%) and shoot (5.82%) biomass in wheat plants under controlled conditions. These results highlight Bacillus velezensis TRQ67 as a promising microbial inoculant with plant growth promotion capabilities and potential antifungal activity against phytopathogenic fungi, as evidenced by strong in vitro antagonistic activity, supporting its further evaluation for sustainable agricultural practices. Full article

Design studio education requires sustained attention and prolonged occupancy of a shared workspace, making environmental opportunities for brief recovery potentially relevant to students’ well-being. This exploratory study used affordance theory as an interpretive lens to examine the environmental modifications students proposed from their assigned desk positions and the extent to which sketch content was associated with academic year, studio location, and desk-level window view access. The dataset comprised 71 structured sketches, geometric view metrics for 85 workstations that were linked to the 71 sketch participants for inferential analyses, and a nonmatched lighting survey administered to the same cohorts one year later (n = 57). Sketches were coded into 17 modification categories. Water and Collaborative/Social Space were retained descriptively but excluded from inferential interpretation because of quasi-complete separation. Fifteen ordinary logistic regression models were evaluated with Benjamini–Hochberg false-discovery-rate correction, together with a complementary specification omitting studio location. Plants (73.2%), natural shapes/patterns (52.1%), and electric light quality (52.1%) were the most frequent modifications. In the primary models, Technology (q = 0.033) and Plants (q = 0.047) retained FDR-supported omnibus fit; only the academic-year coefficient for Technology retained FDR support (OR = 0.17, 95% CI [0.06, 0.50], q = 0.021). The study identifies environmental preferences and hypotheses for future intervention in restorative learning environments research. Full article

Spike density is an important component of wheat spike architecture and is determined by the combined effects of spike-axis elongation and spikelet number. In this study, we characterized an ethyl methanesulfonate (EMS)-induced recessive dense-spike mutant, ds, which was obtained from EMS mutagenesis of the wheat cultivar YN21 in 2013 and displays a field-visible compact spike architecture associated with a shortened spike axis, reduced spike internode spacing, and increased spikelet number. Genetic analysis showed that all F₁ plants exhibited normal spikes and that segregation in the F₂ population fitted a 3:1 ratio, supporting control by a single recessive nuclear gene. Through genome-wide marker-based linkage screening, enlarged-population validation, and fine mapping, ds was delimited to an approximately 864.819 kb interval between ID2B2796 and ID2B7615 on chromosome 2B. This interval contains 11 high-confidence annotated genes, including F-box protein, actin, ubiquitin-conjugating enzyme, ribosomal protein L16, RecX, plant cysteine oxidase, PI4KIIγ, and two adjacent GA3OX-family-related genes. Integrated RNA-seq and RT-qPCR analyses showed that the two adjacent GA3OX-family-related genes were expressed in young spikes and exhibited reduced expression in ds-sib relative to WT-sib. These expression data support their retention as plausible, non-exclusive candidates but do not establish causality. Transcriptome analysis further revealed changes in hormone-related pathways, cell-wall organization, carbohydrate metabolism, and transcription-factor regulation. These results provide a reliable genetic basis for further molecular cloning of ds and suggest that altered hormone- and growth-related transcriptional responses may be associated with dense-spike formation in wheat. Full article