Search Results (1,758)

Identifying superior salt-tolerant germplasm and resistance genes is crucial, as wheat (Triticum aestivum L.) seedlings are highly vulnerable to salt stress. Here, using an optimized 150 mM NaCl treatment, we screened 137 Chinese wheat accessions via an organ-specific method. Phenotyping analysis revealed extensive organ-specific divergence, with 48.91% of accessions displaying inconsistent performance between shoot and root length. We then performed comparative transcriptomics on three representative phenotypes at the seedling stage: Gaoyou 2018, representing the salt dual-sensitive group; Huapei 5, representing the salt dual-tolerant group; and Jimai 60, representing the divergent group with higher tolerance in shoots rather than in roots. Analysis of overlapping differentially expressed genes (DEGs) across all three accessions revealed a basal stress response—characterized by induced osmotic defense and suppressed primary growth—exemplifying a classical growth–defense trade-off. Genotype-specific DEG profiling demonstrated that the divergent Jimai 60 maintains its shoot advantage by reinforcing physical barriers and inhibiting apoptosis. Conversely, transcriptomic profiling implies that the systemically tolerant Huapei 5 maintains coordinated shoot and root tolerance at the seedling stage by strongly activating below-ground Na⁺ homeostasis (efflux and compartmentalization) while simultaneously down-regulating non-essential immune responses to optimize defense energy reallocation. Collectively, our findings provide novel insights into the organ-differentiated salt tolerance of wheat, offering well-characterized elite germplasm and compelling genetic targets for future molecular breeding. Full article

Landslides are the most common geological hazards, and chemical reinforcement is an effective method for enhancing the stability of soil slopes. Based on the coupled Eulerian–Lagrangian method, finite element analyses were conducted to develop a simple design approach for soil slope reinforcement using the curing agent. First, the effects of internal friction angle, cohesion, soil unit weight, slope height and angle on the slope stability were systematically quantified through 93 numerical cases. On this basis, an empirical formula was established for the factor of safety (FOS) of soil slope, and a method for determining the failure mode was proposed using a dimensionless parameter and two critical values related to slope angle. Subsequently, the reinforcement performance of the SH curing agent was investigated by varying the reinforcement position and length. The results indicate that the reinforcement of Case I-II-III and Case I-II provide the best performance, and the optimum reinforcement length was determined for different slope conditions. For slope angles ranging from 25° to 65°, the FOS after reinforcement was found to increase by 12.1% to 18.8% compared with that before reinforcement. Based on the FE results, empirical formulae for predicting the FOS of reinforced slope were further developed. Finally, a simple design approach was proposed for soil slope reinforcement with curing agent. The proposed method provides a convenient and effective reference for engineering practice in soil slope reinforcement with curing agents. Full article

AlCoCrFeNi high-entropy alloy coatings reinforced with different TiN contents (2 wt.%, 4 wt.%, and 6 wt.%) were fabricated on 304 stainless steel by laser cladding. The effects of TiN addition on the microstructure, hardness, friction behavior, and wear resistance of the coatings were investigated. Dry reciprocating sliding tests were conducted under a load of 10 N, a frequency of 5 Hz, a stroke length of 5 mm, and a duration of 20 min using GCr15 bearing steel balls as the counterpart. The results showed that the 2 wt.% TiN coating exhibited the best tribological performance within the investigated composition range, with a microhardness of 579.6 HV_0.5, a relatively low and stable friction coefficient of approximately 0.30–0.35, and a wear rate of 2.9 × 10⁻⁴ mm³/(N·m). When the TiN content increased to 4 wt.% and 6 wt.%, the wear resistance decreased, which was mainly associated with particle agglomeration, local stress concentration, and brittle spalling. These results indicate that appropriate TiN addition can improve the load-bearing capacity and wear resistance of laser-cladded AlCoCrFeNi coatings, providing a potential surface-strengthening strategy for 304 stainless steel components under dry sliding conditions. Full article

To determine a rational, narrow coal-pillar width and support scheme for gob-side entry driving in thick coal seams, the 4904 return airway of the No. 9 coal seam at Zhongshui Coal Mine was investigated using limit-equilibrium analysis, FLAC3D numerical simulation, and field monitoring. The theoretical pillar width was calculated as 6.73–7.90 m, and sensitivity analysis showed that the selected 7 m pillar remained within the reasonable range under variations in key empirical and mechanical parameters. Numerical results indicated that a 7 m pillar could form a relatively complete central load-bearing core and effectively control surrounding-rock deformation, whereas further increasing the pillar width provided limited additional deformation reduction but caused greater coal loss. Compared with the 7 m pillar, the 9 m and 11 m schemes would cause additional coal losses of approximately 9.09 × 10³ t and 1.82 × 10⁴ t, respectively. A hierarchical synergistic support scheme consisting of high-strength bolts, long and short roof cables, and pillar-rib reinforcement cables was proposed. Compared with the equal-length roof-cable-plus-bolt scheme, the proposed scheme provided better control of roof subsidence and rib convergence. Field monitoring showed that roadway deformation gradually stabilized after support installation and remained within a controllable range under the monitored engineering conditions. Full article

Urban traffic congestion remains a persistent global challenge, contributing to significant economic inefficiencies, elevated greenhouse gas emissions, and diminished quality of life. This paper presents a real-world video-based traffic monitoring study combined with a proposed adaptive signal control framework. In the monitoring component, YOLOv11 object detection was applied directly to footage recorded from an overhead bridge position on a 40 km/h road. The model successfully detected and tracked multiple road-user categories, including cars, trucks, buses, motorcycles, cyclists, and pedestrians, yielding 1041 vehicle detections across 25 unique tracked objects. Vehicle speeds were estimated from inter-frame centroid displacement, and a Region of Interest (ROI) occupancy model was used to classify congestion states as High, Medium, or Free Flow using thresholds grounded in Highway Capacity Manual (HCM) level-of-service criteria. The system detected 11 high-congestion frames (3.8%), 184 medium-congestion frames (63.9%), and 93 free-flow frames (32.3%), consistent with moderate congestion observed during the recording period. In the proposed control component, a Proximal Policy Optimisation (PPO)-based reinforcement learning signal controller is designed around the YOLOv11 detection outputs as its state representation. Based on comparable adaptive traffic signal control studies in the literature, the proposed framework is projected to achieve approximately 25% higher peak-hour throughput, 35% shorter queue lengths, and 32% lower average waiting times relative to a fixed-time signal baseline. The detection accuracy (mAP@0.5 = 93.2%) and inference speed (32 FPS) cited are published YOLOv11 benchmarks used as indicative performance references. This work bridges real-world perception and proposed intelligent control, providing a transparent and reproducible methodology for next-generation smart city traffic management. Full article

This study addresses the early-age brittleness and performance limitations of sustainable cement soil. While prior works optimized the baseline compressive strength using recycled sand and nanoclay, the multi-scale synergistic effects of fibers and nanomaterials on the post-peak deformation remain underexplored. To address this gap, a composite modification system incorporating recycled sand, nanoclay, polypropylene fibers, and graphene derivatives was developed. The experimental program comprised standard specimen fabrication, early-age curing, and unconfined compressive strength (UCS) testing, supplemented by RBF neural network curve fitting and quantitative ArcGIS digital image processing of scanning electron microscopy (SEM) micrographs. The results demonstrate that optimizing the fiber parameters (0.6% content with 6 mm length) successfully increases the early UCS to 2263.2 kPa, which is further elevated to a peak of 2755.0 kPa upon co-incorporation with 0.05% small-sized graphene oxide. Correspondingly, a newly introduced ductility index quantitatively confirms that the single-fiber reinforcement yields an index of 1.93, which is further enhanced to 2.02 by the graphene composite system. Microstructure tracking and digital image extraction revealed that the SEM-derived surface porosity decreased significantly, exhibiting a clear inverse relationship with the macroscopic mechanical strength. These quantitative microstructural shifts confirm that graphene effectively filled micropores and reinforced the fiber–matrix interface, establishing a dense matrix network with enhanced interfacial bonding. This multi-scale approach offers a sustainable strategy for green geotechnical applications. Full article

The present study investigates the synergistic effects of incorporating natural alkali-treated Alfa fibers, lime, and ground granulated blast-furnace slag (GGBS) on the physical and mechanical performance of compressed earth blocks. Laboratory tests were conducted using locally sourced earth material, reinforced with two lengths of alkali-treated Alfa fiber—F1 (3–9 mm) and F2 (20–25 mm)—and stabilized with lime (4, 8%) and GGBS (4, 8, 12%). Tests included wet and dry compressive strength, capillary absorption, linear shrinkage, abrasion resistance and thermal conductivity. Results show that the incorporation of Alfa fibers, particularly when combined with lime and GGBS, significantly enhanced wet compressive strength and abrasion resistance, while the initial reduction in dry compressive strength due to fibers was effectively offset by GGBS. The combination of longer Alfa fibers (F2) with lime and GGBS provided the best overall performance, producing compressed earth blocks with superior mechanical strength, durability, and thermal efficiency. Full article

Self-drilling hollow bar micropiles (HBMPs), which integrate drilling, grouting, and reinforcement into a single process, have broad application prospects in mountainous transmission lines and offshore wind power projects. However, existing research has focused mainly on friction piles in soil layers, and there is a lack of systematic understanding of the load-transfer mechanism and bearing capacity calculation method for rock-socketed HBMPs. Based on field static load tests of rock-socketed HBMPs, this study systematically investigates the vertical bearing behavior and capacity calculation method of single rock-socketed HBMPs through a combination of test data analysis, finite element numerical simulation, and theoretical analysis. The field test results show that the load-settlement curves of rock-socketed HBMPs are of a slowly varying type, exhibiting mixed friction-end-bearing characteristics. After data screening, the average Q-s curve of Pile No. 1 and Pile No. 5 was taken as the benchmark, and the representative ultimate bearing capacity of a single pile determined by the 40 mm settlement criterion is 5860 kN. The test data of Pile No. 3 and Pile No. 4 were retained as independent validation data. A three-dimensional finite element model considering the cohesive contact behavior at the pile–rock/soil interface was established using ABAQUS. After calibration with the test results, the error between the simulated and measured bearing capacity is −3.4%, demonstrating good model reliability. Parametric analysis indicates that the bearing capacity increases linearly with the grouting volume increase rate $V_{\mathrm{inc}}$, with the expansion effect being the main enhancement mechanism; the improvement amplitude under hard rock conditions is significantly smaller than that in cohesive soils. The effect of uniaxial compressive strength $q_u$ of hard rock on bearing capacity is negligible because the capacity is controlled by the pile–rock interface shear strength. The bearing capacity increases approximately linearly with the rock-socketed depth $L_r$, and a minimum rock-socketed depth of 1.0 m is recommended. Analysis of the load-transfer mechanism shows that rock-socketed HBMPs rely mainly on shaft resistance (accounting for 90.6%), and the axial force decays significantly along the pile length. Elastic compression of the pile accounts for 78% of the pile head settlement, and the limited displacement at the pile tip leads to insufficient mobilization of end bearing. A modified bearing capacity formula considering the grouting expansion effect is established with shaft resistance as the core. A hierarchical validation strategy is adopted to test its predictive ability: for the finite element cases not participating in parameter calibration, the prediction error is within ±2%; for the field test piles, the prediction error is +7.9%; and for Pile No. 3 and Pile No. 4, the errors are +1.7% and −2.1%, respectively. These values are significantly better than those of existing methods (errors ranging from −72.1% to +54.5%). The research results can provide a theoretical basis for the design of single HBMP bearing capacity under rock-socketed conditions. Full article

Mine tailings are highly disturbed technogenic materials whose low mechanical stability may limit mine closure and long-term land rehabilitation. This study evaluates the geomechanical potential of Chrysopogon zizanioides (L.) Roberty, commonly known as vetiver grass, to improve the shear-strength response of filtered mine tailings under controlled laboratory and numerical modelling conditions. The study does not constitute field-scale validation of phytostabilization; rather, it examines the contribution of vetiver roots to apparent cohesion and shallow slope stability. A combined experimental–numerical framework was implemented, including laboratory characterization of unreinforced and root-reinforced tailings, derivation of Mohr–Coulomb shear-strength parameters, and limit-equilibrium slope-stability analysis under predefined root-growth and root-orientation scenarios. The results indicate that vetiver roots increased apparent cohesion by up to 34.6%, whereas changes in friction angle remained below 10%, suggesting that the dominant reinforcement mechanism is pseudo-cohesive rather than frictional. The calculated factors of safety varied according to slope geometry, assumed root length, root orientation, and simplified water-condition scenarios. However, the findings remain limited to controlled experimental and numerical conditions. Field-scale validation, long-term root monitoring, moisture variability, nutrient availability, phytotoxicity, contaminant immobilization, and life-cycle performance should be assessed before practical implementation. This study provides preliminary geomechanical evidence of vetiver-induced root reinforcement in filtered mine tailings. Full article

This study quantified total biomass stocks in Carrasco (CAR, n = 12), a dense tropical deciduous vegetation type from the Brazilian semi-arid region for which biomass information remains scarce. We also evaluated differences in floristic composition, diversity, structure, and biomass allocation patterns relative to Cerrado sensu stricto (CSS, n = 40). Forest inventories were conducted in southeastern Brazil. Woody biomass was estimated using a regional allometric equation. Roots were sampled in a position adjacent to the plots, and litter was collected at the center of each plot using a frame. Necromass was assessed along a linear transect corresponding to the length of each plot using the line-intersect method. Biomass differences between vegetation types were assessed using generalized linear and mixed-effects models (GLMs and GLMMs). Total biomass reached 45.24 Mg ha⁻¹ in CSS and 59.01 Mg ha⁻¹ in CAR. In CSS, woody biomass predominated (20.47 Mg ha⁻¹; 45%), followed by roots (18.47 Mg ha⁻¹; 41%), litter (5.49 Mg ha⁻¹; 12%), and necromass (0.81 Mg ha⁻¹; 2%). In CAR, roots were the dominant component (32.37 Mg ha⁻¹; 55%), followed by woody biomass (16.57 Mg ha⁻¹; 28%), litter (8.39 Mg ha⁻¹; 14%), and necromass (1.68 Mg ha⁻¹; 3%). CSS and CAR shared only 10% of their species and showed significant differences in total biomass (TB) and belowground biomass (BGB), while aboveground biomass (AGB), aboveground woody biomass (AGWB), litter, and necromass did not differ significantly (α = 0.05). The BGB/AGWB ratio was <1 in CSS and >1 in CAR, resembling global patterns of savanna/shrubland and grassland formations, respectively. Considering the sampling design adopted, despite the higher stem density in CAR, larger individuals in CSS compensated for structural differences, resulting in similar aboveground biomass stocks. Our findings reinforce the floristic and structural distinctiveness of Carrasco and reveal contrasting biomass allocation strategies, with a strong dominance of belowground biomass in CAR. These results demonstrate that aboveground-based assessments can substantially underestimate total biomass in semi-arid transitional vegetation and highlight the need to incorporate non-forest ecosystems into biomass inventories, conservation planning, and climate change mitigation strategies. Full article

Urban traffic congestion remains a persistent challenge for conventional fixed-time signal control, particularly under fluctuating and asymmetric demand. Although multi-agent reinforcement learning (MARL) has shown promise for adaptive traffic signal control, previous studies have often focused on isolated intersections, simplified synthetic networks, or deep-learning-based controllers without systematically comparing tabular and deep-value-based multi-agent approaches under equivalent operating conditions. This study addresses this gap by comparing three traffic signal control strategies: fixed-time control, Multi-Agent Tabular Q-Learning, and multi-agent Deep Q-Network control (MADQN). The evaluation was conducted in a microscopic traffic simulation environment using two complementary testbeds: a synthetic two-intersection corridor, which enables controlled analysis of multi-agent coordination, and a real-world digital twin of the 25 January Corridor in Assiut, Egypt, which tests controller robustness under asymmetric geometry and realistic turning movements. The controllers are assessed under low-, medium-, and high-demand scenarios using queue length, cumulative delay, and Time-To-Collision as operational and safety-related indicators. The results show that MARL-based controllers generally outperform fixed-time control, but their relative performance depends on demand intensity and network complexity. MADQN provides stronger generalization in low-demand and queue-dissipation conditions, whereas Tabular Q-Learning remains highly competitive and can achieve superior delay reduction in several medium- and high-demand cases. These findings indicate that deeper MARL architectures are not universally superior; rather, adaptive signal control deployment should match the controller architecture to the operational objective, traffic demand regime, and practical complexity of the target corridor. Full article

For the sake of investigating the theoretical design optimization of high-rise plane frames, an optimization model was established by taking the minimum top-story lateral displacement as the objective function and treating material strength, story height, and span length as design variables. The design parameters of the frame were optimized using an Assimilation–Competition Quantum-behaved Particle Swarm Optimization (ACQPSO) algorithm. First, the accuracy and computational efficiency of the ACQPSO algorithm were evaluated using four benchmark functions. Then, a five-span, seven-story reinforced-concrete plane frame with a total span of 24 m and a total height of 34 m was taken as a case study. The cross-sectional dimensions of the beams and columns were determined according to relevant design specifications, and the top-story lateral displacement calculated by the D-value method was verified using the Finite Element Method (FEM), confirming its accuracy and effectiveness. Finally, a parametric analysis was carried out to investigate the effects of material strength, story height, span length, and member cross-sectional dimensions on the objective function. The results indicate that story height and column concrete strength have a greater influence on the top-story lateral displacement, whereas the effect of span length is relatively small. In addition, the cross-sectional dimensions of beams and columns affect the top-story lateral displacement more significantly than beam strength. Full article

Real-time monitoring of surface cracks in reinforced concrete (RC) beams is critical to structural safety and service performance evaluation. Current structural crack monitoring still faces prominent scientific and technical bottlenecks: conventional unidirectional sensors cannot achieve multi-directional collaborative sensing, rigid piezoelectric materials exhibit poor compatibility with the large deformation of concrete, and there is a lack of quantitative mapping relationships from sensing signals to crack parameters, making it difficult to simultaneously measure crack width, angle, and morphology. This paper presents a novel ring-shaped piezoelectric sensor based on polyvinylidene fluoride (PVDF) and an annular piezoelectric sensing mechanism for real-time monitoring of crack angle, width, and morphology. The sensor incorporates a laminated structure with four strip sensing units for multi-directional strain detection. Experiments were conducted on RC beams under various loading conditions, and finite element analysis was performed using COMSOL Multiphysics. An innovative crack damage index (B) was introduced to assess structural damage quantitatively. Results demonstrate high sensor sensitivity and stable output. Voltage signals increase both with crack width and crack angle, showing responses of 0.045 mV, 0.041 mV, and 0.023 mV for crack angles of 60°, 45°, and 30°, respectively, at a crack width of 9 mm. Strong consistency between experimental and simulation data validates the effectiveness of the mechanism in monitoring the direction, width, and types of cracks. The crack damage index B exhibits a positive correlation with the structural stress response, enabling a quantitative assessment of damage. This study is applicable to the prestressed concrete box girders and T-beams commonly used in large-span bridges, which are typically with a main span of 20–50 m, a beam length of 6–30 m, a section height of 1.2–2.5 m, and designed for Grade C35–C50 concrete. The findings provide a practical foundation for real-time crack monitoring in large-scale bridge beam members. Full article

As coastal communities face escalating climate risks driven by climate change and biodiversity loss, integrating mangrove ecosystems into sustainability-oriented governance frameworks spanning ecological conservation, climate adaptation, and natural capital accounting has become a global priority. However, quantifying their protection values based on spatiotemporal shoreline dynamics under extreme disturbance remains challenging. Focusing on Dongzhai Harbor (China), this study integrates multi-temporal remote sensing (2010–2021), shoreline evolution analysis, and the Replacement Cost Method to assess ecosystem resilience against Super Typhoon Rammasun in 2014. Results show mangroves exhibited substantial post-disturbance resilience, with only 6.10% area loss following Typhoon Rammasun and 46% natural recovery within six years. Bootstrap confidence intervals for the mangrove-shoreline association overlapped zero across all three temporal periods, indicating that the observational data do not support a statistically confirmed causal protection effect at the landscape scale. This finding underscores that spatially co-occurring ecosystem services do not automatically imply causation, reinforcing the need for empirically grounded valuation in sustainable land-use planning. Because mangroves naturally establish in sheltered environments, the observed spatial overlap between mangroves and the shoreline cannot be interpreted as direct evidence of causal shoreline stabilization. Based on this framework, the potential protection value reached 907.65 × 10⁴ CNY yr⁻¹ across 32.57 km of weighted coastline aligned with mangroves. Notably, erosional segments contributed 50.5% of this value despite comprising only 27.3% of the length, indicating that the replacement-cost estimate is concentrated in erosional segments under the assumed parameters. While acknowledging the need for local biophysical validation and uncertainty analysis in scaling, these findings support integrating dynamic nature-based solutions into territorial planning and Gross Ecosystem Product accounting. The resulting valuation framework offers a replicable pathway for advancing multi-dimensional sustainability encompassing climate-adaptive coastal governance, natural capital integration, and evidence-based coastal spatial planning. Full article

This paper studies long-term value-driven scheduling in multi-layer industrial networks where useful external reward is released mainly at milestone transitions. The delayed feedback causes supervision degeneracy before milestones, because many trajectory prefixes receive the same external return even when their downstream potential is different. We formulate the setting as a time-delayed multi-industrial-chain Markov decision process and present CReSCENT as a milestone-aware structural exploration framework rather than a new reinforcement learning principle. CReSCENT combines macro structural feature construction, contrastive representation learning, milestone-weighted online clustering, and cross-layer credit allocation. It moves intrinsic learning from raw observations to milestone-relevant structural states, then distributes the resulting signal to layers according to their contribution to cross-layer progress. The revised evaluation gives the simulator transition rules, a formal utility metric, an external-reward-only baseline, confidence intervals, and statistical tests. Experiments show that CReSCENT improves utility across layer, task-load, worker-count, and episode-length perturbations against ETA-PSI, EMU, MIMEx, and the external-only baseline. Sensitivity studies further show that the clustering radius and credit-allocation weights have stable operating ranges. Full article