Search Results (145)

Biological control (biocontrol), the use of living organisms to suppress pest populations, has become a cornerstone of sustainable agriculture and a core component of integrated pest management (IPM), offering a vital alternative to over-reliance on chemical pesticides. This review synthesizes recent advancements in the field, covering conceptual frameworks, key influencing factors (landscape structure, plant diversity, climate change), diverse biocontrol agents, and pest-specific case studies. It provides a systematic analysis of critical limitations such as inconsistent efficacy, scalability barriers, regulatory gaps, and pest resistance. To address these gaps, the review places particular emphasis on innovative and integrative strategies as pivotal pathways forward. These include trait-based agent selection, precision landscape design, integrated multi-agent systems, and, prominently, proactive regional management as demonstrated by pre-emptive biological control. The envisioned future directions focus on long-term cross-scale research, optimized production systems, and enhanced stakeholder collaboration aimed at bolstering the practicality and resilience of biocontrol in the face of global climate change. Among these, proactive biological control, which entails the pre-establishment identification and regulatory pre-approval of host-specific natural enemies, stands out as a conceptual model with transformative potential for shortening post-invasion response times and mitigating economic losses, embodying a paradigm shift from reactive to pre-emptive pest management. Full article

The construction sector contributes approximately 39% of global carbon emissions, with embodied carbon—emissions from material extraction, manufacturing, transportation, and construction—representing a systematically underestimated yet increasingly critical component of building life cycle environmental impacts. Traditional Life Cycle Assessment (LCA) methods suffer from static database dependencies, delayed feedback cycles, and limited integration with active construction decision-making, creating a fundamental gap between environmental assessment and construction operations. This paper presents the Digital Twin-Enabled Life Cycle Assessment Framework (DT-LCAF), a dynamic construction-phase embodied carbon accounting system aligned with the EN 15978 standard (stages A1–A5) that integrates Building Information Modeling (BIM), Internet of Things (IoT) sensor networks, and machine learning designed to support real-time sustainability decision-making during smart building construction, with computational performance validated through the offline processing of historical datasets. The framework introduces two enabling mechanisms: (1) a Multi-Scale Carbon Prediction Network (MSCPN) employing hierarchical graph attention networks to capture material interdependencies across component, system, and building scales; and (2) a Reinforcement Learning-based Carbon Optimization Engine (RL-COE) that generates constraint-aware recommendations for material substitution, supplier selection, and construction sequencing while respecting structural, economic, and temporal constraints. Experimental evaluation employs two complementary validation strategies using proxy embodied carbon labels (not ground-truth construction measurements): embodied carbon prediction accuracy is assessed using proxy carbon labels derived from the CBECS dataset (5900 commercial buildings) combined with the ICE Database v3.0 emission factors, achieving a 10.24% MAPE, representing a 23.7% improvement over the best-performing baseline in predicting these proxy estimates; temporal responsiveness and streaming data ingestion capabilities are validated using the Building Data Genome Project 2 (1636 buildings, 3053 m). The RL-COE optimization engine demonstrates an 18.4% mean carbon reduction rate within the proxy label framework across building types while maintaining cost and schedule feasibility. A BIM-based case study illustrates the framework’s construction-phase update loop, showing how embodied carbon estimates evolve dynamically as construction progresses. The limitations regarding the proxy-based nature of embodied carbon labels and the absence of ground-truth construction-phase measurements are explicitly discussed. The framework contributes to smart city sustainability by enabling scalable, data-driven embodied carbon intelligence across building portfolios. All quantitative results are based on proxy embodied carbon estimates derived from building characteristics and standard emission factor databases, rather than measured project data. The reported performance therefore demonstrates a proof-of-concept within the proxy system, and real-project, measurement-based validation remains future work. Full article

The policymakers of Bangladesh have been mapping the energy mix to shift its high dependency on fossil fuels to sustainable energy; wind energy is addressed as a highly potential option. A feasible site selection process is essential for wind power plant establishment; thus, this study aims to identify potential areas for the sustainable development of large-scale wind plants by considering socio-economic, safety and environmental factors. In this study, two techniques of multi-criteria analysis (MCA), analytical hierarchy process (AHP) and ratio scale weighting (RSW), were incorporated with geographic information system (GIS) to select the optimal area in Bangladesh. This study considers fifteen sub-criteria under four main criteria, namely, socio-economy, geology, ecology, and climatology. AHP and RSW assign suitable weights to the sub-criteria based on their significant impact on the plant. GIS analyzes spatial data layers and produces suitability maps with the following categories: 5—most suitable, 4—suitable, 3—moderately suitable, 2—unsuitable, 1—completely unsuitable, and 0—excluded area. The final suitability map was generated using suitability maps of AHP and RSW. Finally, a combination of the final suitability map and the wind speed suitability map provide a total suitable area of 1595.8293 km². This could produce 2.96 GW power with 1418 wind turbines and be able to reduce 4,992,346.42 tons of CO₂ emissions annually (calculated using a reference turbine). The study was uniquely carried out at a 150 m hub height, and integration of AHP and RSW for weight cross-validation was performed for the first time in large-scale wind plant siting in Bangladesh. The findings of the study can be helpful for decision-makers in developing large-scale wind power plants. Full article

This study aims to model and optimize the process parameters influencing the efficiency and yield of oil extraction from Mongolian sea buckthorn seeds using supercritical carbon dioxide (CO₂). The experiments were planned using response surface methodology (RSM) based on a central composite rotatable design (CCRD) to evaluate the effects of extraction pressure, temperature, and time, while maintaining a constant solvent flow rate of 2.0 L/min to balance extraction efficiency and selectivity. Following data refinement and outlier exclusion, the developed second-order polynomial model exhibited excellent accuracy with a coefficient of determination R² of 0.9375. Among the parameters studied, pressure was identified as the most critical factor affecting oil yield. Furthermore, significant interaction effects were observed, particularly between extraction time and the other variables, pressure–time (A * C) and temperature–time (B * C), indicating the time-dependent nature of mass transfer. The predicted optimal conditions for maximum yield were determined to be 5075 psi, 70 °C, and an extraction time of 10 h. Validation experiments under these conditions resulted in an oil yield of 800 g, confirming the reliability of the model. These findings demonstrate the feasibility of optimizing supercritical CO₂ extraction for the industrial-scale production of high-quality functional oils and nutraceuticals. Full article

Pulse-like ground motions can cause severe damage to buried cast iron (CI) pipelines, which necessitates the selection of optimal seismic intensity measures (IMs) to estimate pipeline repair rates. Such a selection is essential for mitigating uncertainty in the seismic risk assessment of buried CI pipelines. For the first time, this study systematically screens the optimal scalar and vector IMs for buried cast iron pipelines with lead-caulked joints under pulse-like ground motions by a symmetrical evaluation based on the criteria of efficiency, sufficiency, and proficiency, providing a new method for reducing uncertainty in pipeline seismic risk assessment. We initiate the study by selecting 124 pulse-like ground motions from the NGA-West2 database and identifying 19 scalar and 171 vector IMs as potential candidates. A two-dimensional soil–pipe model is introduced, incorporating variability in the sealing capacity of lead-caulked joints along the axial direction. CI pipeline repair rates are calculated across various scaling factors and apparent wave velocities, yielding 1116 datasets pertinent to CI pipeline damage. The repair rate is adopted as the engineering demand parameter (EDP) to evaluate the efficiency, sufficiency, and proficiency of candidate IMs. Through comprehensive analysis, peak ground velocity (PGV) and the combination of PGV and the time interval between 5% and 75% of normalized Arias intensity ([PGV, Ds_5–75]) are determined as the optimal scalar- and vector-IMs, respectively, for assessing the repair rate of buried CI pipelines under pulse-like ground motions. Full article

The selection of data smoothing methods is one of the key steps in extracting land surface phenology parameters from time-series remote sensing data. However, existing studies often use default parameters for denoising the time-series data, neglecting the sensitivity of phenology extraction to different combinations of smoothing parameters. Therefore, this study systematically evaluated three parametric smoothing methods—Savitzky–Golay (SG), Whittaker Smoother (WS), and Harmonic Analysis of Time-Series (HANTS)—and two non-parametric methods—Asymmetric Gaussian (AG) and Double-Logistic (DL)—on the accuracy of Start of Season (SOS) and End of Season (EOS) extraction at eight ground phenology observation sites in Inner Mongolia, based on time-series MOD13Q1- Normalized Difference Vegetation Index data and using the derivative method as the background for phenology parameter extraction at the site scale. The results showed that (1) DL and HANTS yielded similar accuracy for phenology extraction in desert steppe, while parametric smoothing methods outperformed non-parametric methods in phenology simulation in typical and meadow steppe regions. (2) We proposed the optimal phenology parameter combination for different steppe types in Inner Mongolia. For desert steppe, DL or HANTS was recommended. For SOS extraction in typical steppe ecosystems, the WS parameter combination was used. For EOS and phenology in meadow steppe, the HANTS parameter combination yielded better simulation results. (3) In desert and meadow steppes, the window radius in SG contributed more to phenology accuracy than polynomial order. The opposite was true for typical steppe. In WS, the contribution of the differential order to SOS and EOS extraction in desert and typical steppes was higher than that of the smoothing factor. The opposite was observed in meadow steppe. In HANTS, the fitting tolerance error was the key factor controlling phenology extraction accuracy. (4) Based on the optimal phenology extraction scheme, the smallest extraction error occurred in meadow steppe at the site scale. This was followed by typical steppe. Desert steppe showed relatively larger errors. This study overcomes the reliance on default parameters in previous studies and proposes a practical framework for phenology extraction for different grassland ecosystems. The findings provide new empirical evidence for method selection and parameter setting in remote sensing phenology monitoring. Full article

The lack of protocols for breaking seed dormancy, inconsistent seed quality, and abiotic stress factors such as drought impede large-scale restoration efforts of pollinator-friendly native plant species. This research explores the germination response, dormancy-breaking techniques, and water stress tolerance in selected pollinator-friendly plant species with characteristics facilitating mechanized rehabilitation protocols and biodiversity enhancement. Forty-two commercial seed lots representing seven plant families with 28 species were evaluated under two alternating temperature regimes (15/25 °C and 20/30 °C) with and without gibberellic acid (GA₃) priming treatments. Six of the twenty-eight species were selected based on pollinator requirements for the monarch butterfly (Danaus plexippus L.) and further examined by priming seeds for 24 h in solutions containing GA₃, kinetin (KIN), and hydrogen peroxide (H₂O₂), or their combinations, to evaluate their dormancy-breaking responses. The effect of water stress on seed germination was assessed in controlled chambers at soil water potentials of −1.08, −0.75, −0.13, and 0 MPa. Initial seed quality of the 42 seed lots revealed that only 62% had greater than 50% germination, while of the same 42 lots, 98% had greater than 50% viability based on the commercial seed label. The difference was largely attributed to seed dormancy. In laboratory studies of the 42 seed lots, GA₃ significantly enhanced germination percentage, and reduced T₅₀ (time to 50% germination) across most seed lots. Overall, germination was higher and faster at 20/30 °C than 15/25 °C. Priming the six selected species with 1.0 mM GA₃ in 0.3% H₂O₂ consistently improved germination compared to the non-primed control after 14 days. Asclepias species (A. incarnata, A. syriaca, and A. tuberosa) exhibited consistently high germination across a broad moisture range of −0.75 to 0 MPa. In contrast, Echinacea purpurea required high moisture levels (−0.13 to 0 MPa) for optimal germination. Monarda fistulosa and Rudbeckia hirta showed their best performance under moderate moisture conditions (−0.13 MPa). Collectively, the use of GA₃ priming to break physiological seed dormancy offers a promising approach to enhance germination and improving the establishment potential of native pollinator species in restoration programs. Full article

Detecting small objects from UAV-captured aerial imagery is a critical yet challenging task, hindered by factors such as small object size, complex backgrounds, and subtle inter-class differences. Single-modal methods lack the robustness for all-weather operation, while existing multimodal solutions are often too computationally expensive for deployment on resource-constrained UAVs. To this end, we propose TAF-YOLO, a lightweight and efficient multimodal detection framework designed to balance accuracy and efficiency. First, we propose an early fusion module, the Two-branch Adaptive Fusion Network (TAFNet), which adaptively integrates visible and infrared information at both pixel and channel levels before the feature extractor, maximizing complementary data while minimizing redundancy. Second, we propose a Large Adaptive Selective Kernel (LASK) module that dynamically expands the receptive field using multi-scale convolutions and spatial attention, preserving crucial details of small objects during downsampling. Finally, we present an optimized feature neck architecture that replaces PANet’s bidirectional path with a more efficient top-down pathway. This is enhanced by a Dual-Stream Attention Bridge (DSAB) that injects high-level semantics into low-level features, improving localization without significant computational overhead. On the VEDAI benchmark, TAF-YOLO achieves 67.2% mAP₅₀, outperforming the CFT model by 2.7% and demonstrating superior performance against seven other YOLO variants. Our work presents a practical and powerful solution that enables real-time, all-weather object detection on resource-constrained UAVs. Full article

Rice lodging is a major agricultural disaster that reduces yield and quality. Accurate lodging detection and causal analysis are essential for disaster mitigation and precision management. To overcome the limited coverage and low automation of conventional approaches, we propose MSR-LodfNet, an enhanced semantic-segmentation model driven by multi-scale remote-sensing imagery, enabling high-precision lodging mapping from regional to field scales. The study selected 13 state-owned farms in Jiansanjiang, Heilongjiang Province, and jointly used PlanetScope satellite images (3 m) and UAV images (0.2 m) to build an integrated workflow of “satellite macro-monitoring, UAV fine verification, and agronomic factor coupling analysis.” The model synergistically optimizes WFNet, DenseASPP multi-scale context enhancement, and Condensed Attention, markedly improving feature extraction and boundary recognition under multi-source imagery. Experimental results show that the model achieves mIoU 84.34% and mPA 93.31% on UAV images and mIoU 81.96% and mPA 90.63% on PlanetScope images, demonstrating excellent cross-scale adaptability and stability. Causal analysis shows that the high-EVI range is significantly positively correlated with lodging probability; its risk is about 6 times that of the low-EVI range, and the lodging probability of direct-seeded rice is about 2.56 times that of transplanted rice, indicating that it may be associated with a higher lodging risk. The results demonstrate that multi-scale remote sensing combined with agronomic parameters can effectively support the mechanism analysis of lodging disasters, providing a quantitative basis and technical reference for precision rice management and lodging-resistant breeding. Full article

Shaft sinking in soft, water-rich strata frequently suffers from low cutting efficiency, cycle-time mismatches between excavation and muck removal, and weak system-level coordination. To elucidate the synergistic mechanisms governing excavation–muck removal interactions and to realize end-to-end performance gains, we investigate the East Ventilation Shaft of the Xinjie Taigemiao mining district as a representative artificial ground freezing (AGF) project. First, drawing on the mechanics of frozen ground and field monitoring, we establish a relationship model linking advance rate, drum rotational speed, cutting depth, and muck production, thereby clarifying why lower rotational speeds, moderate cutting depths, and rational traction reduce energy consumption and mitigate disturbances to the frozen wall. Next, for muck handling, we build a full-process discrete element method (DEM) model, integrate design-of-experiments with response-surface optimization to identify key factors, calibrate contact models, and select collection geometries. The results show that a graded-angle collecting structure improves pile concentration and discharge compliance; combined with a tiered chain-bucket–vertical belt–twin-skip configuration, it delivers matched cycle times and stable “gather–convey–hoist” operation. Finally, two-stage full-scale tests jointly validate excavation and muck removal, demonstrating that the proposed synergy model and optimized parameters sustain continuous, efficient performance across operating conditions. The study provides a reusable mechanistic framework and parameterization blueprint for AGF shaft design and construction. Full article

The leaf area index (LAI) is a key physiological parameter characterizing rice canopy structure and growth status. To face the limits of traditional destructive sampling, which is time-consuming, labor-intensive, and difficult to achieve large-scale dynamic detection, this study proposes a precise UAV-based hyperspectral inversion method for rice LAI using the fusion of Days After Transplantation and Meteorological Factors data (DATaMF). The study framework consisted of three key components: spectral preprocessing (smoothing-R_SG, resampling-R_RS, first derivative transformation-R_FD), spectral feature selection (SPA, CARS, Relief-F), and the construction and assessment of LAI inversion models (RF, ELM, XGBoost) that integrated DATaMF. The results show that (1) the three-level data preprocessing procedure—comprising R_SG, R_RS, and R_FD—coupled with the feature subset selected by the CARS method, demonstrates strong performance in LAI inversion; (2) the incorporation of DATaMF significantly improves rice LAI estimation, leading to improved model accuracy and robustness; and (3) the optimal LAI inversion model is achieved with the RF-based CARS-R_FD-DATaMF approach, yielding test set R², RMSE, and RPD values of 0.8015, 0.5745, and 2.2857, respectively. In conclusion, the hyperspectral LAI inversion method developed in this study, which integrates DATaMF, significantly enhances the model’s accuracy and stability under small-sample conditions. This approach provides reliable technical support for efficient, precise, and dynamic monitoring of rice growth. Full article

Reliable long-term runoff prediction for Wudongde and Three Gorges reservoirs, two major reservoirs in the upper Yangtze River basin, is crucial for optimal operation of cascade reservoirs and hydropower generation planning. This study develops a data-driven model that integrates large-scale climate factors with a Gated Recurrent Unit (GRU) neural network to enhance runoff forecasting at lead times of 7–18 months. Key climate predictors were systematically selected using correlation analysis and stepwise regression before being fed into the GRU model. Evaluation results demonstrate that the proposed model can skillfully predict the variability and magnitude of reservoir inflow. For Wudongde Reservoir, the model achieved a mean correlation coefficient (CC) of 0.71 and Kling–Gupta Efficiency (KGE) of 0.57 during the training period, and values of 0.69 and 0.53 respectively during the testing period. For Three Gorges Reservoir, the CC was 0.67 (training) and 0.66 (testing), and the KGE was 0.52 and 0.49 respectively. The model exhibited robust forecasting capabilities across a range of lead times but showed distinct seasonal variations, with superior performance in summer and winter compared to transitional months (April and October). This framework provides a valuable tool for long-term runoff forecasting by effectively linking large-scale climate signals to local hydrological responses. Full article

To address the challenge of anomaly detection in vibration signals from high-speed electric multiple unit (EMU) motor bearings, characterized by strong non-stationarity and multi-component coupling, this study proposes a synergistic approach integrating variational mode decomposition (VMD) and deep learning. Unlike datasets focused on fault diagnosis (identifying known fault types), anomaly detection identifies deviations into unknown states. The method utilizes real-world, non-real-time vibration data from ground monitoring systems to detect anomalies from early signs to significant deviations. Firstly, adaptive VMD parameter selection, guided by power spectral density (PSD), optimizes the number of modes and penalty factors to overcome mode mixing and bandwidth constraints. Secondly, a hybrid deep learning model integrates convolutional neural networks (CNNs), bidirectional long- and short-term memory (BiLSTM), and residual network (ResNet), enabling precise modal component prediction and signal reconstruction through multi-scale feature extraction and temporal modeling. Finally, the root mean square (RMS) features of prediction errors from normal operational data train a one-class support vector machine (OC-SVM), establishing a normal-state decision boundary for anomaly identification. Validation using CR400AF EMU motor bearing data demonstrates exceptional performance: under normal conditions, root mean square error $(RMSE=0.005)$, Mean Absolute Error $(MAE=0.002)$, and Coefficient of Determination $(R^2=0.999)$; for anomaly detection, accuracy = 95.2% and F1-score = 0.909, significantly outperforming traditional methods like Isolation Forest (F1-score = 0.389). This provides a reliable technical solution for intelligent operation and maintenance of EMU motor bearings in complex conditions. Full article

In this investigation, pinocembrin and galangin were efficiently extracted from the male inflorescence of Populus alba L. × berolinensis K. Koch through an enzymatic pretreatment–ultrasonic-assisted strategy (EP-UAS), and the feasibility of their pilot-scale application was validated. The optimal parameters (ethanol volume fraction, cellulase dosage, incubation temperature, incubation time, pH, liquid‒solid ratio, ultrasonic irradiation power during incubation, duty cycle, ultrasonic irradiation power and time during extraction) affecting pinocembrin and galangin yields were systematically explored. The Box–Behnken design (BBD) results provided optimal parameters for the EP-UAS process. Under the optimal conditions, the actual yields of pinocembrin and galangin were 2158.33 ± 0.13 μg/g and 1257.96 ± 0.06 μg/g, respectively. Stability, recovery and reproducibility were determined under the above optimized conditions to evaluate the proposed EP-UAS method. Moreover, laboratory-scale experimental results revealed that the conditions selected via single-factor and response surface experiments were also applicable to pilot-scale production, facilitating industrialization. Full article

Investigating rainfall infiltration mechanisms and slope stability dynamics under varying vegetation cover conditions is essential for advancing ecological slope protection methodologies. This research focuses on large-scale outdoor slope models, with the objective of monitoring soil moisture variations in real-time during rainfall events on four types of slopes: bare, herbaceous, shrub, and mixed herb–shrub planting. Combining direct shear tests for unsaturated soil with numerical simulations, and considering the weakening effect of water on shear strength, this study analyzes slope stability. The findings reveal significant spatial variations in rainfall infiltration rates, with maximum values recorded at a burial depth of 0.2 m, declining as the burial depth increases. Different types of vegetation have distinct impacts on slope infiltration patterns: herbaceous increases cumulative infiltration by 21.32%, while shrub reduces it by 61.06%. The numerically simulated moisture content values demonstrate strong congruence with field-measured data. Compared with monoculture herbaceous or shrub root systems, the mixed herb–shrub root system exhibits the most significant enhancement effects on shear strength parameters. Under high water content conditions, root systems demonstrate substantially greater improvement in cohesion than in internal friction angle. Before rainfall, shrub vegetation contributed the most significant improvement to the safety factor, increasing it from 2.766 to 3.046, followed by herbaceous and mixed herb–shrub vegetation, which raised it to 2.81 and 2.948. After rainfall, mixed herb–shrub vegetation demonstrated the greatest enhancement of the safety factor, elevating it from 1.139 to 1.361, followed by herbaceous and shrub vegetation, which increased it to 1.192 and 1.275. The study offers preliminary insights and a scientific basis for the specific conditions tested for selecting and optimizing eco-friendly slope protection measures. Full article