Search Results (685)

Visible fracture detection in underground roadways is challenging because fracture targets are often elongated, weakly contrasted, irregularly distributed, and easily confused with complex rock-wall textures. In addition, uneven illumination, dust interference, and blurred boundaries further reduce the reliability of conventional crack detection and segmentation methods. To improve fracture instance segmentation under such conditions, this study proposes YOLOv13n-seg-crack, an improved lightweight instance segmentation model based on a self-constructed YOLOv13n-seg baseline. The proposed model introduces three main improvements. First, a C2f-CA module is embedded into the backbone to enhance spatial-position perception and directional feature representation for elongated fractures. Second, a shallow high-resolution branch and auxiliary feature paths, denoted as B2 + H2 + P2, are constructed to strengthen the transmission of fine edge and texture information for small and discontinuous fracture targets. Third, an Edge-aware SIoU (EA-SIoU) loss is designed by adding edge-consistency and aspect-ratio constraints, thereby improving bounding-box localization for narrow and irregular fracture regions. Experiments were conducted on the public Crack Segmentation Dataset and an expanded self-built underground roadway dataset collected at the Woniushan Experimental Base. On the public dataset, YOLOv13n-seg-crack achieved detection Precision, Recall, mAP50, and mAP50:95 of 84.56%, 65.49%, 71.51%, and 52.72%, respectively, and mask Precision, Recall, mAP50, and mAP50:95 of 74.94%, 60.38%, 59.52%, and 21.99%, respectively. Compared with YOLOv13n-seg, the detection mAP50 and mask mAP50 increased by 1.91 and 3.19 percentage points, respectively, while the model maintained an inference speed of 168.73 FPS. Repeated-seed experiments, ablation studies, and degraded-image tests further demonstrate the stability and robustness of the proposed improvements. On the self-built underground roadway dataset containing 100 images and 118 annotated fracture instances, YOLOv13n-seg-crack improved detection mAP50 from 68.72% to 73.36% and mask mAP50 from 30.76% to 33.74%. These results indicate that the proposed method provides an effective and lightweight solution for visible fracture detection and instance segmentation in complex underground roadway scenes. Full article

Large distributed sensor arrays require repeated recalibration as radiation damage, material aging, gain variation, and readout drift alter channel responses. We studied a high-granularity calorimeter as a large sensor array and addressed unsupervised recalibration from two unpaired datasets: a nominal reference response and an aged response with attenuated cell-wise signals. Aging was modeled by a deterministic sensor-wise base field with reading-level stochastic variation; the base coefficients were used only for post-training evaluation. We evaluated a Wasserstein adversarial calibration field against an evaluation-only global-mean coefficient predictor and two non-adversarial estimators, a per-cell mean-energy ratio and independent per-cell Wasserstein matching. The adversarial objective supplied an adaptive event-level discrepancy over the full sensor array. In a hierarchical data-efficiency study with four reference/aged sampling-seed combinations and three configured-seed adversarial fits per seed combination, the adversarial method achieved an RMSE from $0.0238\pm 0.0014$ to $0.0173\pm 0.0012$ and a positive $R^2$ from $0.828\pm 0.022$ to $0.910\pm 0.013$ across the tested event counts. Its RMSE was also below the approximately $0.0579$ global-mean reference at every event count, demonstrating the recovery of cell-wise coefficient variation beyond the global mean. The mean-energy-ratio and Wasserstein-only estimators remained below the $R^2=0$ reference in this sparse benchmark. Full article

Remote-sensing object detection commonly requires repeated training on high-resolution aerial and satellite imagery, where targets may be small, densely distributed, and surrounded by extensive background. For remote-sensing detection tasks that require shorter model-training cycles, reducing training time without sacrificing detection accuracy is important. The Anti-Forgetting Sampling Strategy (AFSS) reduces training time by avoiding repeated processing of learned images, but its fixed warm-up may start sampling before the detector is mature and thereby reduce accuracy. We propose Maturity-Gated AFSS (MG-AFSS), a detector-maturity activation controller for AFSS. The method accumulates validation mean average precision at an intersection-over-union threshold of 0.50 (mAP50), fits a cumulative saturating curve online, and enables AFSS only when the estimated maturity indicates trustworthy image states. After activation, it reuses the original AFSS sampling rule. We evaluate MG-AFSS with matched lightweight detectors on five public remote-sensing datasets: NWPU VHR for the main three-seed study and additional settings; UCAS-AOD for horizontal-box detection; HRSC2016 and ShipRSImageNet for oriented bounding-box (OBB) boundary cases; and DOTAv1 for a larger-scale OBB block. In the three-seed, 80-epoch, from-scratch NWPU VHR experiment, MG-AFSS improves over AFSS by +0.0264 mAP50 and +0.0319 mAP50–95, where mAP50–95 denotes mean average precision averaged over intersection-over-union thresholds from 0.50 to 0.95. It keeps mAP50–95 essentially equal to standard full-dataset training (+0.0003), with a training-time ratio of 0.867 relative to standard training. In a pretrained NWPU setting, the training-time ratio is 0.557 relative to matched standard training, with a higher checkpoint mAP50. In the DOTAv1 block, MG-AFSS has a training-time ratio of 0.759, lower than AFSS at 0.777; its checkpoint-best differences from standard training are −0.0095 mAP50 and −0.0149 mAP50–95, both smaller in magnitude than those of AFSS. Overall, MG-AFSS yields a conditional accuracy–training-time trade-off: it reduces training time when the maturity condition is satisfied and, as observed in the two smaller OBB boundary runs, retains full-dataset training otherwise. Full article

This article reviews the reactive magnetron sputtering of piezoelectric Aluminium Nitride (AlN) thin films, with a focus on process parameter optimisation and system-level deposition challenges. AlN is a leading material for MEMS and RF applications owing to its c-axis (002) orientation, high acoustic velocity, wide bandgap (∼6.2 eV), and CMOS compatibility. We review the influence of sputtering power, nitrogen flow ratio, substrate temperature, and target-to-substrate distance on crystallographic quality and document practical hardware challenges, including vacuum leakage, grounding faults, target erosion, and mass flow controller drift, that critically affect reproducibility but are systematically underreported in the literature. A perspective is provided on emerging application domains where optimised AlN films address current performance gaps, including next-generation RF/telecom systems towards 6G and Future Networks, harsh environment sensing and actuation, biomedical ultrasound, and IoT energy harvesting. The complementarity between AlN and Silicon Carbide (SiC) is discussed for high-temperature, high-power, and radiation-hard MEMS, where AlN/SiC heterostructures combine the piezoelectric activity of AlN with the mechanical and chemical robustness of SiC. It also incorporates a discussion of dopant- and heteroepitaxy-based AlN engineering, AlN deposition on a wider range of substrates, the role of seed and electrode underlayers, and pulsed-DC sputtering as a third power supply mode alongside RF and conventional DC. Full article

Seismic trace interpolation reconstructs missing traces caused by incomplete spatial sampling while preserving reflection-event continuity, amplitude fidelity, and waveform character. Randomly missing traces introduce an inherent reliability asymmetry: observed positions contain measured amplitudes, whereas missing positions must be inferred from neighboring seismic events. In U-Net-based encoder–decoder architectures, shallow features extracted around zero-filled missing traces may carry sampling artifacts and unreliable high-frequency details through direct skip connections. To address this limitation, this paper proposes MGF-UNet, a mask-guided gated skip fusion network for 2D seismic interpolation with randomly missing traces. The incomplete seismic patch and binary trace mask are used as a dual-channel input, and mask-guided gates regulate encoder features before decoder fusion. Known-trace preservation is then applied to retain measured traces in the final output, while a hybrid objective supervises missing-trace recovery, full-patch fidelity, and structural coherence. In five-run experiments on Marmousi synthetic data, MGF-UNet achieves the lowest mean MissingRMSE at the 30% and 50% missing ratios and remains competitive in global RMSE, SSIM, and SNR; however, the seed-matched paired comparisons do not reach statistical significance. Field-data comparisons and local waveform analyses further show coherent event reconstruction and reduced residual artifacts. These results suggest that mask-guided skip regulation is a promising strategy for moderate-to-severe random missing-trace interpolation. Full article

This study evaluated LED-supported speed-breeding conditions for two registered soybean cultivars (‘Sonya’ and ‘Victoria’; approximately Maturity Groups III–IV) by testing four constant photoperiod regimes (10, 14, 18, and 22 h light) combined with four LED spectral treatments differing in high red (HR; 640–660 nm), deep blue (DB; 450–460 nm), far-red (FR; 720–730 nm), cool white (CW; 6500 K; 400–700 nm) and warm white (WW; 3000 K; 400–700 nm) components. Photoperiod treatments were implemented as sequential runs in a controlled growth-chamber (28 ± 2 °C; ≥50% RH; 400–600 µmol·m⁻²·s⁻¹ PPFD), and performance was compared with a field trial in Antalya, Türkiye. Developmental timing (R1, R6), plant height, stem dry weight, pod number, seed oil concentration, fatty acid composition, and seed germination were analyzed using mixed-effects and generalized linear mixed models. Photoperiod exerted dominant control over developmental timing. The 10 h photoperiod regime produced the fastest progression (R1 ≈ 24.5 DAS; R6 ≈ 50 DAS), whereas 18 h markedly delayed R1 and R6 (with one LED treatment within 18 h failing to reach R6), and 22 h did not reach R6. LED spectral treatment did not affect R1 or R6 within photoperiods, but influenced plant height, stem dry weight, total seed oil, and germination. Field-grown plants produced substantially higher biomass and pod number than growth chamber-grown plants, consistent with greater branching and pod set under open-field, direct-sown conditions. Total seed oil concentration differed by environment and cultivar with no environment × cultivar interaction, while centered log-ratio PERMANOVA indicated largely stable fatty acid proportional composition across environments and LED treatments. Germination analyses (restricted to 10 h and 14 h due to insufficient seed production) showed significant effects of LED treatment and harvest timing, with the highest germination under LED–3. Overall, our results indicate that a constant 10 h photoperiod, combined with immature seed harvest from approximately R6 + 20 onward, provides an effective strategy for accelerating soybean generation turnover while maintaining high germination capacity. LED spectral design can also be used to modulate plant architecture and seed quality without substantially altering developmental timing. Full article

The growing demand for natural food colorants has increased interest in sustainable alternatives derived from agro-industrial by-products. This study developed and characterized a food-grade natural brown colorant from date seed waste of Phoenix dactylifera L. (Khalas variety) by process optimization, mineral profiling, phenolic characterization and analysis of its storage stability. A food-grade aqueous extraction system was employed, and a Taguchi L9 orthogonal array was used to optimize the processing parameters affecting color development. Roasting temperature was identified as the most influential factor, contributing 85.75% of the variation in color intensity. Signal-to-noise ratio analysis determined the optimum processing conditions as 200 °C roasting temperature, 5 min roasting time, and 15 min extraction time, yielding an OD₄₂₀ value of 0.927. FTIR analysis confirmed the presence of hydroxyl, carbonyl, aromatic, and carbohydrate-associated functional groups related to thermally generated brown pigments. Further studies demonstrated that both temperature and opaqueness of the storage bottle significantly influence colorant stability. Refrigerated storage in amber containers provided superior retention of total phenolic and flavonoid contents, in addition to a superior antioxidant activity compared with room-temperature storage in clear containers. Mineral profiling revealed significantly high levels of potassium, calcium, phosphorus, and magnesium, with the mineral composition remaining largely stable during storage. HPLC analysis identified catechin and gallic acid as the major phenolic compounds, while ellagic acid was detected exclusively in the glycerol–water extract, indicating enhanced recovery of selected phenolics through solvent modification. Overall, the developed date seed colorant combines color-forming, antioxidant and nutritional properties, while providing a sustainable approach for the valorization of date-processing by-products. The findings highlight the potential application of date seed colorant as a clean-label alternative to synthetic brown colorants and support the development of sustainable food ingredients aligned with sustainable development goals. Full article

Sowing date and seeding rate are key agronomic measures for regulating yield formation and quality accumulation in winter wheat, especially in the context of global warming, which has led to delayed sowing becoming the norm. Clearly identifying optimal combinations of sowing date and seeding rate under late-sowing conditions is crucial for achieving coordinated improvement in both high yield and quality. Therefore, this study used the strong-gluten wheat cultivar ‘Shaannong 33’ and conducted a two-factor split-plot field experiment in the southern North China Plain (Nanyang, Henan) during 2019–2021. Three sowing dates (early sowing: 27 October; intermediate sowing: 31 October–2 November; late sowing: 6–8 November) and four seeding rates (180 × 10⁴, 240 × 10⁴, 300 × 10⁴, and 360 × 10⁴ plants ha⁻¹) were tested to systematically analyze the effects of sowing date and seeding rate on wheat yield and its components, grain processing quality, and starch physicochemical properties. The results showed that sowing from late October to early November (approximately 25 October to 2 November in 2019–2020 and 25 October to 31 October in 2020–2021) combined with a seeding rate of 240–300 × 10⁴ plants ha⁻¹ maintained yield at a comparably high level across both growing seasons while improving processing quality indicators such as protein content, wet gluten content, and dough stability time for the strong-gluten cultivar ‘Shaannong 33’. However, the optimal sowing window varied between years: in 2019–2020, both D1 (27 October) and D2 (2 November) produced comparable yields, whereas in 2020–2021, D2 (31 October) was superior for both yield and quality. This inter-annual variability highlights the influence of climatic conditions on the optimal sowing window. Further delaying the sowing date to after 6 November led to decreases in yield and quality parameters; however, increasing the seeding rate to 360 × 10⁴ plants ha⁻¹ partially compensated for yield loss. In addition, late sowing significantly increased amylopectin content, swelling power, and pasting indicators such as peak viscosity and final viscosity while decreasing amylose content and pasting temperature, which is beneficial for improving cooking and eating quality. Correlation analysis indicated that the amylose/amylopectin ratio was the core factor determining starch pasting properties, while protein content showed a significant negative correlation with starch pasting indicators, reflecting the resource competition effect between grain protein and starch. Yield differences between the two years were mainly driven by changes in spike number per unit area, while inter-annual climate fluctuations (especially precipitation and temperature) significantly affected spike formation capacity. Overall, sowing date and seeding rate have a synergistic regulatory effect on yield and quality of late-sown wheat for the strong-gluten cultivar ‘Shaannong 33’. It is recommended to adopt a sowing date of 25–31 October with the seeding rate controlled at 240–300 × 10⁴ plants ha⁻¹ to achieve synergistic improvement of yield and quality under late-sowing conditions in the southern North China Plain for this cultivar. Full article

The efficient utilization of natural resources and agricultural wastes aligns well with the UN Sustainable Development Goals. Sunflower seed husks are an affordable and renewable source of cellulose that can be used as an alternative to wood-based resources. However, the yield and quality of cellulose are affected by the presence of components such as phytomelanin, hemicellulose, and lignin. In this study, cellulose pulp (CP) was extracted from untreated, water-treated, and water and alkali-treated SFH. The optimal peroxyacetic acid (PAA) to biomass ratio was established to assess the influence of pre-treatment on CP properties. Water and alkali pre-treatments significantly increased CP yield and reduced residual lignin, hemicellulose, and ash compared to untreated samples. The optimal yield of CP for SFH-NaOH was 55.73%. All microcrystalline cellulose (MCC) types exhibited comparable α-cellulose content, confirmed by the IR band at 1430 cm⁻¹. XRD showed lower crystallinity in untreated CP-SFH relative to pre-treated samples. SEM revealed porous fibrous structures across all MCCs. Pre-treatment also improved the thermal stability and ζ-potential of cellulose nanocrystals (CNCs) obtained from MCC, without altering morphology. CNC yields were determined for all three CP variants. The CP-SFH-NaOH sample had the maximum CNC yield of 52.12%. Phytomelanin with admixture of lignin was recovered from alkaline extracts (8.56%) and fully characterized. Overall, the findings demonstrate the potential of integrated SFH utilization to produce high-quality cellulose derivatives and phytomelanin with admixture of lignin. Full article

The compressive strength of ultra-high-performance concrete (UHPC) is jointly influenced by multiple factors, including material composition, mixture proportion parameters, and curing regime. Conventional empirical methods are therefore insufficient to accurately characterize the highly nonlinear relationships involved. To improve the prediction accuracy of UHPC compressive strength and to achieve mixture proportion optimization that simultaneously considers mechanical performance, economic efficiency, and environmental impact, this study developed random forest (RF), artificial neural network (ANN), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) models based on 810 publicly available UHPC experimental datasets. Model performance was evaluated using R², RMSE, MAE, and MAPE. To enhance the robustness of model validation, repeated K-fold cross-validation, sensitivity analysis with different random seed splits, and benchmark model comparisons were further introduced. The results indicate that the XGBoost model achieved superior predictive performance on both the test set and robustness validation, with test-set R², RMSE, MAE, and MAPE values of 0.9604, 7.77, 5.58, and 4.80, respectively. The model was further interpreted using SHAP, PDP, and ICE methods, and the results revealed that curing age, fiber content, silica fume content, and water-to-binder ratio were important variables affecting the compressive strength of UHPC. Furthermore, XGBoost was used as a surrogate model and coupled with NSGA-II and TOPSIS methods for multi-objective optimization. Under the constraints of compressive strength, water-to-binder ratio, superplasticizer-to-binder ratio, and absolute volume, a computationally recommended UHPC mixture proportion balancing strength, cost, and carbon emissions was obtained. This study provides a reproducible machine-learning-assisted approach for UHPC compressive strength prediction and low-carbon, cost-effective mixture proportion design. Full article

The global transition toward plant-based diets has driven the inclusion and legumes as primary sources of proteins and micronutrients. However, the raw whole seed hosts complex matrices of antinutritional factors and crystalline starch arrangements that limit proteolytic digestibility, chelate essential minerals, and induce accelerated postprandial glycemic responses. Conventional culinary and thermal treatments applied in isolation are frequently insufficient to disrupt the physicochemical matrix of the seeds, leaving critical gaps regarding how to sustainably optimize mineral bioaccessibility and convert water-soluble starches into stable slowly digestible fractions. This scoping review synthesizes analytical evidence demonstrating that targeted fermentative bioprocessing acts as a microstructural modulator. However, these biochemical outcomes are not unidirectional; the expansion of nutritional value is strictly governed by a complex interplay of substrate properties, process moisture, pH adjustments, and thermal pretreatments in plant defense frameworks and spatially reorganizing starch polymers. Microbial organic acid production and the mechanical penetration of fungal hyphae promote a 90–100% degradation and elimination of phytates and condensed tannins, eliminating non-digestible galacto-oligosaccharides and inactivating trypsin inhibitors. These mechanisms optimize phytate-to-mineral molar ratios, doubling the bioaccessibility of iron, zinc, and calcium in the digestive aqueous phases, while microbial beta-glucosidase expression bioconverts conjugated glycosides into free aglycones with high antioxidant activity. Simultaneously, the induction of molecular retrogradation drives continuous increases in the resistant starch fraction, inducing significant reductions in the hydrolysis index and lowering the predictive glycemic index to low thresholds. These findings consolidate controlled fermentation as a viable biotechnological intervention, providing structural guidelines for the rational design of functional foods, biofortified baked goods, and vegan beverages with high digestive tolerance. Full article

Periodic resource reservation underpins vehicular sidelink traffic, yet its compatibility is inherently asymmetric. In New Radio (NR) sidelink Mode 1, two configured grant (CG) streams collide if and only if the greatest common divisor (GCD) of their periods divides their offset difference, so coprime periods cannot be separated by any offset on a single subchannel. Admission opportunity is thus governed by the arithmetic of the period set, not by load alone—a disparity we call schedulability asymmetry. We propose Divisor-Aligned Period Decomposition (DAPD), which fixes a common divisor d and reduces coexistence to a closed-form GCD test: periods that are multiples of d are admitted directly, and incompatible ones are reshaped into a d-aligned substitute within the delay budget rather than rejected. This levels the schedulability asymmetry at its source for any catalog period whose delay budget admits a d-compatible substitute: once every request is represented on a common divisor, no period is structurally disadvantaged, so such a period can be admitted regardless of its arithmetic relationship to the others. Under a collision-only scheduler model that isolates deterministic reservation collisions from physical-layer effects, every admitted CG is collision-free by construction. An extension, DAPD+, salvages the resulting slack with dynamic grants that never overlap an admitted CG. In simulation over 1000 seeds across vehicle densities up to subchannel saturation, DAPD+ attains the highest overall packet delivery ratio among all evaluated schedulers, including an oracle offset optimizer and a recent multi-configuration CG scheduler, while holding admitted-CG delivery at one and keeping far more per-vehicle delivery ratios above target, where the reactive baseline’s per-vehicle reliability collapses with density. DAPD+ thus serves the largest volume of periodic traffic at high reliability without sacrificing aggregate throughput, equalizing admission opportunity across heterogeneous periods. Full article

Beetroot pomace is a valuable food-processing by-product that is rich in betalains and phenolic compounds, but the instability of these bioactives limits their direct use in functional foods. This study aimed to develop a pumpkin seed protein-based encapsulated ingredient from beetroot pomace extract and evaluate its preliminary application in yogurt fortification. Beetroot pomace contained 193.75 ± 3.83 mg GAE/100 g DW of total phenolics and 95.78 ± 1.27 mg/100 g DW of total betalains. Encapsulation was optimized using the response surface methodology, with the wall-to-core ratio, extract dilution, and mixing time as independent variables. The optimal encapsulate showed experimentally confirmed encapsulation efficiencies of 75.37% for phenolics and 84.02% for betalains, containing 196.62 ± 4.37 mg GAE/100 g total phenolics and 53.19 ± 0.90 mg/100 g total betalains. After simulated gastrointestinal digestion, betalains remained detectable at 43.15 ± 1.46 mg/100 g, while total phenolics increased to 726.56 ± 30.59 mg GAE/100 g and DPPH antioxidant activity reached 1472.76 ± 7.58 mg TE/100 g, indicating the improved extractability of phenolics from the protein matrix. The encapsulate showed low water activity and moisture content but high hygroscopicity and very poor flowability, indicating the need for further powder-handling optimization. Yogurt fortification with 3% encapsulate, selected as a preliminary technologically feasible level, improved the bioactive profile during storage at 4 °C for 7 days and −18 °C for 21 days. These results support pumpkin seed protein-encapsulated beetroot pomace bioactives as sustainable multifunctional ingredients for yogurt fortification, while further sensory validation and comparison with free extracts are required. Full article

Byzantine-resilient multi-agent reinforcement learning (MARL) matters in networked cyber-physical systems, where corrupted sensor messages degrade formation accuracy and execution-time safety. This paper presents an evaluation and audit study: a multiplicity-corrected operating-regime protocol applied to RS-MARL, a representative trust-based safety pipeline. The aim is to identify supported, inconclusive, and detector-limited regimes rather than claim a universally superior new MARL algorithm. The evidence base contains a 3000-run core matrix over five methods, six attack families, five Byzantine ratios, and 20 seeds per cell; 580 benign-control and ablation runs; and a 2380-run review-audit extension covering A-CBF calibration, four-switch ablation, sensor impairment, and high-seed confirmation. Results are regime-specific. RS-MARL has lower mean safety violations than Safe-MAPPO in 19 of 30 attack-ratio cells, but no core contrast survives Holm correction. Detection is reliable under collusive, random, and stealthy attacks, but weak or undefined under constant, adaptive, and sign-flip attacks, which bound the current energy-based trust detector’s operating envelope. A-CBF margin retuning does not improve over the deployed setting after correction, while four-switch ablation identifies SET as independently necessary for collusive-attack detection. The results support a reproducible reporting template: matched baselines, sensitivity estimates, detection reliability, artefact audits, and explicit safety-performance trade-offs. Full article

Pomegranate (Punica granatum L.) is widely valued for its rich nutritional profile and distinctive sensory characteristics. As one of the oldest cultivated fruits, it has an extensive history of cultivation in China and possesses abundant germplasm resources. Nevertheless, systematic evaluation of these resources remains inadequate, limiting progress in germplasm innovation and utilization. In this study, we analyzed 16 fruit quality traits across 142 pomegranate accessions. Most traits showed wide phenotypic variation, with coefficients of variation (CVs) ranging from 2.10% to 108.83%. Notably, titratable acidity (TA) and anthocyanin content showed high variability (coefficient of variation, CV > 78%), while fruit shape index and total soluble solids (TSS) showed relatively low variability (CV < 10%). Cluster analysis delineated three distinct phenotypic groups. The first group comprised accessions characterized by large fruit size, thick peel, high acidity, and soft seeds. The second group exhibited high seed hardness, low acidity, and elevated sugar-acid and TSS–acid ratios. The third group displayed reduced levels of bioactive compounds such as tannins, phenols, and anthocyanins, combined with high seed hardness. Correlation analysis followed by principal component analysis (PCA) extracted six principal components, and based on comprehensive scoring, SXXA23, AHHB13, SD41, SXXA27, AHHB40, HS1, AHHB8, HY22, HN4, and AH27 were identified as priority accessions for further evaluation within this repository panel. Full article