Search Results (668)

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems. Full article

The paper proposes an adaptive architecture of an electromechanical drive with internally controlled energy exchange, implemented through the integration of an inertial flywheel and a controlled clutch into the structure of a planetary transmission. A multi-mass dynamic and energy model of the system is developed, and the power balance is verified. Based on the energy formulation, adaptive energy and predictive energy control strategies are implemented. The results of numerical simulation confirm that the use of the internal energy exchange loop increases system stability, reduces peak motor torque by 30–40%, decreases maximum output speed deviations by 35–45% under step load conditions, and reduces the root-mean-square tracking error by 20–30% compared with reactive energy-based control, demonstrating improved tracking performance and reduced actuator load compared to the classical drive architecture. Full article

Mediator is a central transcriptional coactivator that connects sequence-specific transcription factors with RNA polymerase II to control inducible gene expression in plants. MED16 is a Mediator tail module subunit that functions as a context-dependent integrator, helping coordinate developmental programs with environmental adaptation. This review summarizes current evidence for MED16 function from structural and evolutionary perspectives to physiological outputs, with emphasis on how MED16 interacts with transcription factors and other Mediator subunits to shape RNA polymerase II engagement at target loci. In terms of development, MED16 contributes to organ growth and root system architecture, and comparative studies have revealed that it plays conserved roles in lineage-specific wiring. Under abiotic stress, MED16 supports the efficient activation of stress-inducible transcription, including cold acclimation and nutrient stress responses such as phosphate starvation-dependent root remodeling. In immunity, MED16 modulates salicylic acid- and jasmonate/ethylene-associated defence outputs and can be targeted by plant viruses, which is consistent with its role in antiviral transcriptional responses. Mechanistically, MED16 participates in cooperative and competitive interactions within the Mediator complex that tune hormone-responsive outputs, exemplified by MED25-related competition in abscisic acid signalling. We highlight key limitations and future directions, including the need for mechanistic validation beyond Arabidopsis, clearer models of dosage control in crops, improved understanding of context-dependent tail configurations, and high-resolution mapping of MED16 interaction interfaces. Full article

Phosphorus (P) is an essential macronutrient for plant growth and a major limiting factor for crop productivity, especially in tropical soils characterized by low P availability and high fixation capacity. The strong dependence of modern agriculture on non-renewable phosphate fertilizers, combined with their low use efficiency, raises economic and environmental concerns and reinforces the need to improve phosphorus use efficiency (PUE) in maize. PUE is a complex trait governed by integrated morphophysiological, biochemical, and molecular mechanisms related to phosphorus acquisition, internal remobilization, metabolic reprogramming, and root system plasticity. Recent advances using omics-based approaches have substantially expanded the understanding of these mechanisms, revealing coordinated regulation of carbon and energy metabolism, phosphatase activity, redox balance, and root meristem dynamics under P-limiting conditions. In parallel, increasing evidence demonstrates the important role of phosphate-solubilizing and plant growth-promoting bacteria in enhancing P availability through organic acid secretion, enzymatic mineralization of organic P forms, and modulation of root architecture. However, despite these advances, the genetic basis of plant responsiveness to beneficial bacteria and the interaction between host genotype and microbial activity remain poorly explored. This review integrates current knowledge on phosphorus dynamics in the soil–plant system, the genetic control of PUE in maize, and the contribution of beneficial bacteria, highlighting the importance of combining classical breeding, molecular approaches, and microbial strategies to accelerate the development of maize cultivars with improved phosphorus efficiency and reduced fertilizer dependency. Full article

Water scarcity, poor soil, and low water and fertilizer utilization are major challenges on agricultural production in the tableland region of China’s Loess Plateau. Optimizing tillage patterns and improving soil nutrient status can improve crop yield and water and fertilizer utilization efficiency. A field trial was initiated in 2005 to assess the impacts of various tillage and fertilization practices on dryland agricultural production. A split-plot design was used, with tillage practices (traditional tillage and no tillage) as the main plot treatment and fertilization management (no fertilization (CK), mineral nitrogen (N), mineral phosphorus (P), composted cow manure (M), a combination of mineral nitrogen and phosphorus (NP), and a combination of mineral nitrogen, phosphorus, and composted cow manure (NMP)) as the split-plot treatment. An experiment was conducted from 2022 to 2024. The NMP treatment resulted in lower bulk density, a lower three-soil-phase index, and higher mean weight diameter, geometric mean diameter, soil water storage, total nitrogen, and soil organic matter than the CK. In the no-tillage treatment, the crop roots were less effective at extracting water from the deep subsoil, leading to greater residual moisture at depth (especially in the 120–200 cm soil layer) and lower yield and water use efficiency than in traditional tillage. The grain yield and water use efficiency were 9.2% and 8.4% lower, respectively, under no tillage than under traditional tillage. The NMP under traditional tillage exhibited lower surface soil bulk density and a higher three-soil-phase index, mean weight diameter, geometric mean diameter, soil organic matter, total nitrogen, and water use efficiency than the unfertilized control, resulting in higher grain yields. The NMP under traditional tillage is recommended to increase grain yield and water use efficiency in wheat–maize rotation systems in the tableland region of China’s Loess Plateau. Future studies should analyze the deep root architecture and the effect of weed competition on soil water depletion. Full article

A primary bottleneck in deploying Fiber Bragg Grating (FBG) sensors lies in their inherent dual sensitivity to thermal and mechanical variations, which mandates robust decoupling mechanisms for precise parameter extraction. To address this persistent cross-sensitivity issue, this study introduces a novel interrogation scheme that integrates a Convolutional Neural Network with a Bidirectional Long Short-Term Memory (CNN-BiLSTM) architecture. Instead of relying on conventional peak-tracking algorithms or isolated central wavelengths, our proposed data-driven strategy directly mines structural features from the full reflection spectra, thereby substantially mitigating cross-interference errors. The experimental results reveal that the coefficients of determination (R²) for strain and temperature prediction reach 99.37% and 99.75% each, while the root mean square errors (RMSEs) are 13.51 µε and 1.42 °C, respectively. The proposed method requires only a single FBG sensor, which reduces the sensor requirements, showing great potential in sensing applications requiring low costs and high adaptability. In addition, in some special environments, temperature information cannot be obtained, so we utilize another reference FBG to realize the temperature compensation. Meanwhile, we proposed a spectral differencing method (SDM) by differencing the spectra of the two FBGs to obtain the spectra containing only strain information and sent them as a dataset for model training, with a 4-times improvement in accuracy over traditional compensation methods. Finally, we also explored the application of the system for distributed FBGs, achieving an absolute peak wavelength interrogation precision of approximately ±0.02 nm. The system is expected to be applied in the field of structural health monitoring, which is promising even in harsh environments. Full article

Seed germination and early seedling development are critical determinants of crop establishment, stress tolerance, and yield stability, yet these stages remain insufficiently integrated into contemporary crop improvement strategies. Recent advances across genome editing, microbiome-assisted seed treatments, nanotechnology-enabled priming, and artificial intelligence-guided phenotyping have generated substantial but fragmented insights into early developmental regulation. This review synthesizes recent advances across early plant development research. It demonstrates that seemingly diverse technologies converge on a limited set of regulatory control nodes, including abscisic acid–gibberellin balance, redox homeostasis, and root system architectural plasticity. By integrating evidence from molecular, microbial, physicochemical, and computational studies, early plant ontogeny is presented as a tunable regulatory state governed by quantitative thresholds rather than as a strictly predetermined genetic process. Advances in deep learning, reinforcement learning, and high-throughput phenotyping further enable the modeling and optimization of early developmental trajectories across genotype by environment contexts. Together, these insights establish early development as a programmable target for crop improvement and provide a mechanistic foundation for designing integrated interventions that enhance developmental uniformity, stress resilience, and yield stability across diverse agroecological systems. Full article

The increasing scale and structural flexibility of modern wind turbine rotors have made real-time monitoring and active control of blade tip deflection a critical requirement for ensuring operational safety, particularly regarding blade-tower clearance. Since direct measurement through physical sensors is often impractical due to high costs, installation difficulties and maintenance challenges, this work proposes a data-based framework for out-of-plane blade tip deflection estimation. The approach introduces a systematic and hierarchical input selection framework that evaluates sensor signal groups, ranging from standard SCADA measurements to configurations including auxiliary nacelle/tower sensors and dedicated blade-root instrumentation. By combining Spearman correlation and spectral coherence, the proposed framework ensures consistent representation of key turbine dynamics across all operating regions. This framework provides a structured trade-off between implementation feasibility and estimation fidelity, enabling tailored solutions for applications such as structural health monitoring and safety-critical active control. Compact Feedforward Neural Network (FNN) and Time-Delay Neural Network (TDNN) architectures, whose hyperparameters are optimized via Bayesian optimization, are employed to achieve high estimation accuracy while preserving computational efficiency. Evaluated through high-fidelity aeroelastic simulations of the NREL 5 MW turbine using the industry-standard FAST (Fatigue, Aerodynamics, Structures, and Turbulence) tool across all operating conditions, the approach achieves $R^2=0.894$ using SCADA-only inputs, $R^2=0.973$ when augmented with nacelle and tower-top sensors and a peak fidelity of $R^2=0.989$ using blade-root bending moment data. These results demonstrate that high-fidelity virtual sensing is attainable without blade instrumentation, providing a viable pathway for real-time tip clearance monitoring and fatigue mitigation. This directly enhances the operational resilience of wind energy systems and their contribution to the stability of renewable-dominated power grids. Full article

Seed germination and early root growth are decisive for crop establishment, yet responses to ionic environments can be strongly genotype-dependent. This study evaluated the effect of supplementing an agar-based in vitro system with a commercial NPK fertilizer on the germination dynamics and early seedling traits of Raphanus sativus L. Seeds were tested in two solid media: A (1.3% agar, no fertilizer) and AF (1.3% agar supplemented with 0.45 g of granular NPK fertilizer (15–30–15) per 200 mL medium), using a completely randomized 3 × 2 factorial design. Germination percentage and synchrony are key constituents of seedlot evaluation because they jointly capture both viability and the temporal coordination of emergence. However, final germination percentage alone does not reflect the timing and uniformity of germination, which can be critical for predicting establishment and subsequent performance. Therefore, indices such as mean germination time (MGT), coefficient of velocity of germination (CVG), and interval germination rates are frequently employed to describe germination dynamics. In addition to germination dynamics, early seedling morphometry (e.g., root and hypocotyl traits) can provide complementary information on early vigor and stress sensitivity under contrasting media or environmental conditions. Root elongation was significantly reduced by fertilization in ASD and GE, whereas AS exhibited consistently shorter roots with no significant response. PCA summarized 86.3% of the total variance in the first two components, separating treatments along a vigour/architecture axis and a germination capacity axis (%G), and hierarchical clustering identified five response groups. Overall, a low-cost agar + fertilizer system effectively discriminated genotype-specific sensitivity to an ionic environment during early establishment, highlighting the need to consider variety-dependent thresholds when using commercial fertilizers for in vitro screening. Full article

Drought reduces soil moisture and impairs root function, posing a significant threat to rice production in arid regions. The influence of soil amendments on early rice root development under semi-dry cultivation remains insufficiently characterized, especially when assessed using non-destructive rhizotron techniques. This study employed a scanner-based rhizotron system to evaluate early root responses of rice seedlings to six amendments under semi-dry irrigation: vermicompost and peat moss, spirulina powder, gypsum, rice husk biochar, zeolite, and an unamended control. The vermicompost plus peat moss (VC+PM) treatment demonstrated the highest water-holding capacity (26%), root projected area (9.60 cm² plant⁻¹), and root surface area (84.79 cm² plant⁻¹). VC+PM also promoted extensive lateral branching (233 secondary and 1709 tertiary roots) and the greatest total lateral root length (363.09 cm plant⁻¹), resulting in superior biomass (shoot: 140.00 mg plant⁻¹; root: 56.70 mg plant⁻¹) and the lowest root-to-shoot ratio (0.90). These improvements are attributed to the enhanced moisture retention of peat moss and the nutrient and phytohormone contributions of vermicompost. In contrast, rice husk biochar exhibited the lowest water-holding capacity (14%), while other amendments produced moderate or limited effects. The results establish a direct relationship between improved soil water retention and early-stage drought-avoidant root development. The combination of VC and PM emerges as a promising approach to enhance root plasticity and seedling establishment in water-saving rice systems. As this study was conducted under controlled rhizotron conditions and limited to the seedling stage (20 days after sowing), future research should prioritize multi-season field trials to assess yield translation and economic feasibility assessments to support farmer adoption. Full article

Drought and soil nutrient deficiency are critical constraints on plant growth and ecological restoration in desert steppes; however, the interactive mechanisms between arbuscular mycorrhizal fungi (AMF) and phosphorus fertilization remain poorly elucidated. To investigate the regulatory mechanisms governing root system architecture (RSA) remodeling and regrowth capability in Agropyron under drought stress, a controlled experiment was conducted using two genotypes: Inner Mongolia (NM) and Xinjiang (XJ). The experimental design comprised three water regimes (70%, 50%, and 30% field capacity [FC]), two P levels (P0, P1), and two inoculation treatments (A0, A1). The results indicated the following: (1) Although drought significantly inhibited Agropyron growth, the combined application of AMF and P (A1P1) induced a highly significant synergistic effect, augmenting total aboveground biomass by 66.08–160.58% compared to the control. This synergy exhibited distinct “environmental dependency,” being most pronounced under moderate drought conditions (50% FC). (2) Mechanistic analysis revealed that A1P1 optimized RSA by significantly increasing total root length, root surface area, and root volume (e.g., total root length increased by 281.4–375.1% under severe stress), thereby enhancing water and nutrient acquisition. (3) The A1P1 treatment significantly mitigated the decline in regrowth potential induced by successive clipping, sustaining a higher tiller number (increasing by up to 1.8-fold in the 3rd clipping). (4) The XJ genotype was characterized by higher basal biomass and root investment “high-yield phenotype”, whereas the NM genotype demonstrated greater sensitivity to AMF-P regulation “highly responsive phenotype”. In conclusion, the synergistic interaction between AMF and P mitigates drought stress by reshaping RSA and enhancing regrowth capability, providing a theoretical basis for the efficient management of arid grasslands. Full article

Abiotic stresses such as drought and heat increasingly threaten plant growth and ornamental quality, particularly in climate-sensitive floricultural crops. Arbuscular mycorrhizal fungi (AMF) are known to enhance plant resilience under such conditions, yet their role in lilies remains insufficiently explored. In this study, we used a two-tier experimental approach to evaluate AMF-mediated benefits in lilies. First, different AMF strains, namely Funneliformis mosseae (FM), Rhizophagus intraradices (RI), Rhizophagus irregularis (RIG), Claroideoglomus etunicatum (CE), Diversispora versiformis (DV), and a mixed consortium (MIX), were screened for growth-promoting effects in two Lilium species, Taiwan lily and Lilium cv. Sorbonne, under non-stress conditions. Second, a selected AMF–host combination from the screening was evaluated to improve tolerance to drought, heat, and combined drought + heat stress. Among the tested strains, DV and MIX showed the most consistent improvements across key growth traits and root colonization. In the stress experiment, stress treatments reduced growth and physiological performance, particularly under combined drought + heat. AMF inoculation enhanced plant performance by improving shoot and root biomass, improving root system architecture, and leading to a higher chlorophyll content, greater relative water content, and enhanced flower traits. Biochemical analyses further revealed that AMF mitigated stress-induced oxidative damage by reducing reactive oxygen species (ROS) accumulation, as shown by reduced O₂•⁻ and H₂O₂ staining. This reduction in oxidative stress was supported by increased activities of key antioxidant enzymes, indicating that AMF activate cellular defense mechanisms. These findings underscore the potential of AMF as a sustainable biotechnological tool for improving stress tolerance in lilies and enhancing floricultural productivity under climate-challenged environments. Full article

The integration of Internet of Things (IoT) and mobile computing in education offers new avenues to address complex health behaviors that affect cognitive performance. While traditional health education relies on passive information delivery, emerging research suggests that interactive systems can bridge the gap between intent and action. This study addresses the “double burden of malnutrition” in Ecuadorian schoolchildren (N = 120) as a Human-Computer Interaction (HCI) challenge. By utilizing a quantitative profiling approach rooted in the Social Dimensions of Health framework, we modeled the user requirements for a proposed intervention system. The findings identified a critical “Action Gap”: while 78.3% of users possess the motivation to improve habits for academic gain, 53.3% remain entrenched in high-sugar consumption patterns due to environmental latency. Statistical profiling reveals a significant dissonance ($p<0.05$) between cognitive intent and behavioral execution. Consequently, this paper presents the “Digital Bridge Architecture,” a computational framework that leverages these motivation metrics to design an Alternate Reality Game (ARG) logic. We conclude that conventional static applications may be limited in their capacity to support sustained behavioral change in this context. The proposed framework suggests that context-aware, gamified feedback mechanisms can offer a promising direction for aligning academic motivation with healthier behavioral outcomes. Full article

Phosphorus, a non-renewable nutrient and limiting factor for crop growth, has drawn considerable attention due to the need to improve its use efficiency. Intercropping enhances phosphorus use efficiency by increasing biodiversity, thereby maintaining high productivity and ecosystem sustainability. The primary mechanisms through which intercropping systems enhance phosphorus uptake and utilization in crops encompass adaptive modifications in root morphology, the secretion of a variety of root exudates (including organic acids, phytosiderophores, and phosphatases), and the recruitment of beneficial microorganisms, such as plant growth-promoting rhizobacteria (PGPR). In this context, the remodeling of root architecture increases the soil contact area, while root exudates not only directly mobilize soil phosphorus reserves but also supply energy and signaling molecules to microorganisms, facilitating the targeted assembly of rhizosphere communities. These microorganisms further augment phosphorus transport and uptake through a series of processes involving “chemical dissolution, enzymatic mineralization, and hyphal transport.” This review systematically explores the synergistic interaction between root architecture and exudates in promoting efficient phosphorus utilization, with the aim of enhancing the understanding of the regulatory mechanisms governing subterranean inter root interactions. Future research should investigate the biological underpinnings of subterranean interactions within intercropping systems that improve phosphorus efficiency. This can be achieved by concentrating on gene interaction networks associated with phosphorus uptake, transport, and utilization; rhizosphere metabolites; beneficial functional microorganisms; real-time monitoring of high-throughput phenotypes; and simulations and optimizations based on artificial intelligence. Full article

In arid northwest China, water scarcity is the primary constraint on agricultural sustainability. Accurate prediction of soil moisture under vegetation is essential for optimizing water use and enabling precision irrigation. Furthermore, water and nitrogen management are often studied in isolation, and their spatiotemporal synergy in regulating soil moisture remains unclear, which hinders the development of optimized coupled strategies. To address this, this study integrated UAV hyperspectral (450–950 nm), multispectral remote sensing, and ground sensor networks to systematically conduct field experiments covering three irrigation levels: full irrigation (W1) at 100% of maintaining soil moisture content; mild deficit irrigation (W2), with soil moisture content set at three-quarters of W1; and severe deficit irrigation (W3), with soil moisture content set at half of W1 and three nitrogen application rates (N1: 350, N2: 250, and N3: 150 kg/ha) in a field experiment. Through sensitive band extraction and spectral index optimization, triple-band indices (RES: Reflectance Extraction Index, MSR: Moisture Sensitive Ratio Index, two novel triple-band spectral indices developed based on Kubelka–Munk and Hapke models) were innovatively developed to enhance signals and suppress noise. Random Forest algorithms were employed to construct soil moisture inversion models for different soil layers. Rigorous comparative analysis comprehensively evaluated performance differences between hyperspectral and multispectral technologies in the indirect retrieval of soil moisture based on crop physiological response and detecting soil moisture at varying depths (10–100 cm). The results indicate that the 450–760 nm visible band represents the optimal spectral region for soil moisture detection. The two indices (MSR and RES) constructed within this range demonstrated prediction correlations 18–32% higher than traditional indices. Hyperspectral technology exhibited comprehensive advantages, particularly in monitoring deep soil layers (>80 cm) (R² = 0.49 vs. 0.18 for multispectral). The spatiotemporal dynamics of soil moisture are primarily governed by irrigation intensity, while nitrogen fertilizers indirectly influence water redistribution through physiological processes such as root architecture regulation, rather than directly altering soil water-holding capacity. This study demonstrates the efficacy of a UAV-based hyperspectral system for precision soil moisture monitoring in vegetated farmland, and it provides a critical scientific basis for optimizing water–nitrogen management and enhancing water use efficiency in arid agriculture. Full article