Search Results (71)

This study aimed to determine the effects of diammonium phosphate (DAP) and yeast autolysates (organic nutrients) added during alcoholic fermentation on the content and profile of aroma compounds in Sauvignon Blanc wines. Sequential additions of either DAP or organic nutrients were applied mainly during the first half of fermentation, increasing yeast assimilable nitrogen (YAN) from an initial 124 mg N/L to final concentrations of 208 and 209 mg N/L for DAP and yeast autolysates, respectively. Control musts were fermented without nutrient supplementation. All treatments were fermented using commercial yeast strain. Varietal thiols, ethyl and acetate esters, higher alcohols, glutathione (GSH), and YAN were monitored at early, mid, and late stages of fermentation, as well as in the final wines. Varietal thiols were formed at early stages of fermentation in all treatments; however, concentrations of both 4-methyl-4-sulfanylpentan-2-one (4MSP) and 3-sulfanylhexan-1-ol (3SH) were higher in wines supplemented with organic nutrients comparing to DAP and control. Compared to the control, DAP and organic nutrient supplementation increased ethyl ester concentrations in wine by 40.2% and 26.9%, respectively. Both nutrient treatments also resulted in higher acetate ester concentrations, while total higher alcohols were reduced by 19.1% with DAP and 12.1% with organic nutrients. No significant differences in GSH concentrations were observed among treatments. Sensory analysis revealed that wines supplemented with DAP achieved the highest scores for tropical aroma, varietal aroma, and overall quality. Overall, sequential supplementation with either inorganic or organic nitrogen positively influenced fermentation kinetics and aroma compound composition, resulting in improved varietal expression of Sauvignon Blanc wines. However, in low-YAN musts, DAP had a greater impact than organic nitrogen sources and should therefore be considered a key strategy for ensuring an adequate yeast nitrogen status. Full article

Enhancing cereal grain quality while maintaining yield stability represents a pressing global challenge for sustainable agricultural development. Optimizing grain quality in cereal crops, which account for more than 60% of global dietary energy, relies heavily on managing nitrogen dynamics during the heading and grain-filling stages. Late-stage nitrogen application (from heading to early grain-filling stages) optimizes the temporal dynamics of nitrogen supply and exhibits substantial regulatory potential in mediating the yield–quality trade-off. Nitrogen availability can profoundly influence source–sink dynamics, carbon–nitrogen metabolic coordination, and the biosynthesis of storage reserves. This systematic review consolidates current understanding of the molecular and physiological mechanisms by which late-stage nitrogen application affects grain development and final quality in cereals, with a particular focus on major cereal crops including wheat, rice, and malting barley, which represent contrasting quality objectives and nitrogen management requirements. At the physiological level, late-stage nitrogen application delays functional leaf senescence, sustains photosynthetic carbon assimilation capacity, facilitates assimilate transport and partition to developing grains, and optimizes the accumulation dynamics and compositional profiles of starch and protein. At the molecular level, this review elucidates the sequential regulatory cascades governing nitrogen signal perception and transduction, the coordinated transcriptional networks underlying carbon–nitrogen metabolic crosstalk, and the expression dynamics of genes encoding starch biosynthetic enzymes and storage proteins. Integrating those recent research advances, this review also highlights several critical challenges currently facing the field. To address these challenges, we delineate promising avenues for future research including constructing time-series multi-omics frameworks, employing genome-editing technologies to functionally validate key regulatory genes and integrating artificial intelligence and big data analytics. The goal of this review is to establish a theoretical basis for precision nitrogen management strategies designed to optimize cereal crop production, targeting high yield, superior quality, and improved nitrogen use efficiency concurrently. Full article

Polar sea ice is undergoing rapid change, with recent record-low extents in both hemispheres, raising the demand for skillful predictions from days to seasons for navigation, ecosystem management, and climate risk assessment. Accurate sea ice prediction is essential for understanding coupled climate processes, supporting safe polar operations, and informing adaptation strategies. Physics-based numerical models remain the backbone of operational forecasting, but their skill is limited by uncertainties in coupled ocean–ice–atmosphere processes, parameterizations, and sparse observations, especially in the marginal ice zone and during melt seasons. Statistical and empirical models can provide useful baselines for low-dimensional indices or short lead times, yet they often struggle to represent high-dimensional, nonlinear interactions and regime shifts. This review synthesizes recent progress of DL for key sea ice prediction targets, including sea ice concentration/extent, thickness, and motion, and organizes methods into (i) sequential architectures (e.g., LSTM/GRU and temporal Transformers) for temporal dependencies, (ii) image-to-image and vision models (e.g., CNN/U-Net, vision Transformers, and diffusion or GAN-based generators) for spatial structures and downscaling, and (iii) spatiotemporal fusion frameworks that jointly model space–time dynamics. We further summarize hybrid strategies that integrate DL with numerical models through post-processing, emulation, and data assimilation, as well as physics-informed learning that embeds conservation laws or dynamical constraints. Despite rapid advances, challenges remain in generalization under non-stationary climate conditions, dataset shift, and physical consistency (e.g., mass/energy conservation), interpretability, and fair evaluation across regions and lead times. We conclude with practical recommendations for future research, including standardized benchmarks, uncertainty-aware probabilistic forecasting, physics-guided training and neural operators for long-range dynamics, and foundation models that leverage self-supervised pretraining on large-scale Earth observation archives. Full article

This paper presents an updatable stochastic geometallurgical framework that integrates geochemical compositions and processing-related variables within a unified spatial modeling and data assimilation workflow. The framework combines multivariate geostatistical simulation with real-time updating based on the Ensemble Kalman Filter, allowing stochastic realizations to be sequentially adjusted as new production data become available. The methodology accounts for geological uncertainty, compositional constraints, and multivariate dependencies. This is achieved by combining the isometric log-ratio transformation with flow anamorphosis within a multivariate Gaussian framework. As a result, compositional geochemical variables and metallurgical responses can be updated consistently while preserving their physical and statistical relationships. The framework is demonstrated using the Gol Gohar iron ore deposit as a case study. Exploration drill hole data and production-scale blast hole measurements are assimilated within an ore control context. The results indicate that the update-enabled simulation approach reduces prediction errors and spatial uncertainty, while capturing complex, non-linear relationships among geometallurgical variables. The framework is generic and can be applied to other deposits where real-time integration of geological, geochemical, and processing information is needed to support operational decision-making. Full article

Landslide deformation monitoring plays a critical role in geohazard prevention and risk mitigation in mountainous regions, where timely and reliable deformation information is essential for early warning and disaster management. Monitoring landslide deformation in mountainous areas remains a persistent challenge, largely due to rugged topography, dense vegetation cover, and low interferometric coherence—factors that substantially limit the effectiveness of conventional InSAR methods. To address these issues, this study aims to develop a robust time-series InSAR framework for enhancing deformation detection and measurement density under low-coherence conditions in complex mountainous terrain, and accordingly introduces the Sequential Estimation and Total Power-Enhanced Expectation–Maximization Inversion (SETP-EMI) approach, which integrates dual-polarization Sentinel-1 SAR time series within a recursive estimation framework, augmented by polarimetric coherence optimization. This methodology allows for dynamic assimilation of SAR data, improves phase quality under low-coherence conditions, and enhances the extraction of distributed scatterers (DS). When applied to Zhenxiong County, Yunnan Province—a region prone to geohazards with complex terrain—the SETP-EMI method achieved a landslide detection rate of 94.1%. It also generated approximately 2.49 million measurement points, surpassing PS-InSAR and SBAS-InSAR results by factors of 22.5 and 3.2, respectively. Validation against ground-based leveling data confirmed the method’s high accuracy and robustness, yielding a standard deviation of 5.21 mm/year. This study demonstrates that the SETP-EMI method, integrated within a DS-InSAR framework, effectively overcomes coherence loss in densely vegetated plateau regions, improving landslide monitoring and early-warning capabilities in complex mountainous terrain. Full article

Concentrated wastewater streams, like vacuum blackwater (VBW), pose significant management challenges due to their high organic strength and pathogen loads. This review evaluates an integrated biorefinery model employing sequential black soldier fly larvae (BSFL) bioconversion and thermophilic anaerobic digestion (TAD) as a circular solution for effective VBW management. The BSFL pretreatment facilitates bio-stabilization, mitigates ammonia inhibition via nitrogen assimilation, and initiates contaminant degradation. However, this stage alone does not achieve complete hygienization, as it fails to inactivate resilient pathogens, including helminth eggs and spore-forming bacteria, thus precluding the safe direct use of frass as fertilizer. By directing the frass into TAD, the system addresses this limitation while enhancing bioenergy recovery: the frass serves as an optimized, nutrient-balanced substrate that increases biomethane yields, while the sustained thermophilic conditions ensure comprehensive pathogen destruction, resulting in the generation of a sterile digestate. Additionally, the harvested larval biomass offers significant valorization flexibility, making it suitable for use as high-protein animal feed or for conversion into biodiesel through lipid transesterification or co-digestion in TAD to yield high biomethane. Consequently, the BSFL-TAD synergy enables net-positive bioenergy production, achieves significant greenhouse gas mitigation, and co-generates digestate as sanitized organic biofertilizer. This cascading approach transforms hazardous waste into multiple renewable resources, advancing both process sustainability and economic viability within a circular bioeconomy framework. Full article

Climate change poses a major challenge for wine production in semi-arid regions, where grape ripening frequently leads to excessive sugar accumulation and reduced acidity. This study evaluated the combined effect of cold pre-fermentative maceration (PM) and the use of non-Saccharomyces yeasts (Lachancea thermotolerans and Torulaspora delbrueckii) on the composition and sensory properties of Cayetana Blanca wines. Pre-fermentative maceration increased titratable acidity by 0.5 g/L and yeast-assimilable nitrogen by 28 mg/L, creating more favorable conditions for the metabolic activity of non-Saccharomyces species. Wines fermented with L. thermotolerans—especially in sequential inoculation with S. cerevisiae after PM—showed the highest acidity and lactic acid content (2 g/L), together with 1% v/v lower ethanol and 1 g/L higher glycerol than the control. These wines were perceived as fresher and better balanced, despite a moderate decrease in fruity esters such as ethyl hexanoate, ethyl octanoate, and isoamyl acetate. Cluster analysis confirmed that non-Saccharomyces fermentations developed distinct compositional profiles only when combined with PM. Overall, the PM + L. thermotolerans + S. cerevisiae treatment achieved the most favorable balance between acidity, ethanol, and sensory freshness. This approach provides a sustainable and readily applicable method to enhance acidity and freshness in white wines from warm-climate regions. Full article

The pronounced spatial heterogeneity of underlying surface characteristics within the parallel ridge–valley system of eastern Sichuan necessitated hydrological discretization of the watershed into nested subdomains comprising inter-ridge valley units and secondary slope cells. A distributed flood simulation framework specifically adapted to parallel ridge–valley topography was developed, coupled with a sequential intelligent parameter optimization algorithm. Model validation was conducted through the simulation of ninety flood events (2015–2023) in the Lishui watershed, a representative parallel ridge–valley basin. For parameter-calibrated sub-watersheds, mean relative errors of 13.8% (peak discharge) and 12.3% (runoff depth) were achieved, while non-calibrated watersheds exhibited marginally higher inaccuracies at 14.6% and 15.1%, respectively. Spatial parameter estimation was effectively implemented through the assimilation of limited hydrometeorological station data. The integrated modeling framework, incorporating terrain-adaptive parameterization and intelligent calibration protocols, demonstrated high-fidelity flood process simulation capabilities in complex parallel ridge–valley landscapes. Full article

Accurate and efficient modeling of subsurface flow in reservoir simulations is essential for optimizing hydrocarbon recovery, enhancing water management strategies, and informing critical decision-making processes. However, traditional numerical simulation methods face significant challenges due to their high computational cost and limited scalability in handling large-scale models with uncertain geological parameters, such as permeability distributions. To address these limitations, we propose a novel deep learning-based framework leveraging a conditional U-Net architecture with time embedding to improve the efficiency and accuracy of reservoir data assimilation. The U-Net is designed to train on permeability maps, which encode the uncertainty in geological properties, and is trained to predict high-resolution saturation and pressure maps at each time step. By utilizing the saturation and pressure maps from the previous time step as inputs, the model dynamically captures the spatiotemporal dependencies governing multiphase flow processes in reservoirs. The incorporation of time embeddings enables the model to maintain temporal consistency and adapt to the sequential nature of reservoir evolution over simulation periods. The proposed framework can be integrated into a data assimilation loop, enabling efficient generation of reservoir forecasts with reduced computational overhead while maintaining high accuracy. By bridging the gap between computational efficiency and physical accuracy, this study contributes to advancing the state of the art in reservoir simulation. The model’s ability to generalize across diverse geological scenarios and its potential for real-time reservoir management applications, such as optimizing production strategies and history matching, underscores its practical relevance in the oil and gas industry. Full article

The primary objective of this study is to facilitate the conversion of inorganic selenium (Se) into organic Se within plants via assimilation, subsequently feeding it to livestock and poultry to enhance healthy animal production and yield Se-enriched livestock and poultry products. Therefore, it is imperative to first investigate the impact of varying Se doses on the agronomic traits of plants as well as their forage storage and processing. This experiment investigated the effect of Se fertilizer application on the fermentation quality, chemical composition, and bacterial community of Pennisetum americanum × Pennisetum purpureum cv Minmu 7 (HPM7). There were nine Se fertilizer dissolution levels of HPM7 treated, which were 0 mg/kg (Se0), 0.50 mg/kg (Se1), 1.00 mg/kg (Se2), 2.00 mg/kg (Se3), 5.00 mg/kg (Se4), 10.00 mg/kg (Se5), 20.00 mg/kg (Se6), 30.00 mg/kg (Se7), 40.00 mg/kg (Se8), and 50.00 mg/kg (Se9). The results showed that after silage, the water-soluble carbohydrates of Se1, Se2, and Se3 were lower than Se0, and the pH of Se3, Se4, and Se6 were lower than the Se0. The number of OTUs in the nine groups was sequentially Se1 > Se2 > Se3 > Se8 > Se6 > Se5 > Se7 > Se4 > Se0. The dominant bacterial phyla in silage samples were Firmicutes and Proteobacteria. Compared with Se0, Bacterial Shannon index in Se1 and Se2 were higher, while Chao1 and ACE indices of Se1, Se2, Se3, Se5, and Se6 were higher. A beta diversity analysis indicated that the Se1 exhibited the highest number of significant biomarkers. Escherichia coli between Se0 and Se3 and Clostridium sardiniense and Clostridium perfringens between Se0 and Se1 exhibited significant differences at a species level. The most abundant pathways for metabolism were membrane transport, carbohydrate metabolism, translation, replication, repair, and amino acid metabolism. The correlation analysis indicated that the dry matter content was negatively correlated with Bacillus (p < 0.01), Lactobacillus (p < 0.05), Pediococcus (p < 0.05), and Hirschia (p < 0.05); the contents of neutral detergent fiber and hemi-cellulose were positively correlated with Lactobacillus (p < 0.05, p < 0.01). The protein content was negatively correlated with proteus (p < 0.05). This study demonstrated that the application of Se fertilizer could enhance the Se content in HPM7. The optimal fertilization concentration was found to range from 0.50 to 2.00 mg/kg, which facilitates the metabolism of soluble carbohydrates and enhances both the fermentation quality and microbial relative abundance of HPM7 silage. Full article

Understanding the dynamics of physiological changes involved in the acclimation responses of plants after their exposure to repeated cycles of water stress is crucial to selecting resilient genotypes for regions with recurrent drought episodes. Under such background, we tried to respond to questions as: (1) Are there differences in the stomatal-related and non-stomatal responses during water stress cycles in different clones of Coffea canephora Pierre ex A. Froehner? (2) Do these C. canephora clones show a different response in each of the two sequential water stress events? (3) Is one previous drought stress event sufficient to induce a kind of “memory” in C. canephora? Seven-month-old plants of two clones (’3V’ and ‘A1’, previously characterized as deeper and lesser deep root growth, respectively) were maintained well-watered (WW) or fully withholding the irrigation, inducing soil water stress (WS) until the soil matric water potential (Ψ_msoil) reached ≅ −0.5 MPa (−500 kPa) at a soil depth of 500 mm. Two sequential drought events (drought-1 and drought-2) attained this Ψ_msoil after 19 days and were followed by soil rewatering until a complete recovery of leaf net CO₂ assimilation rate (A_net) during the recovery-1 and recovery-2 events. The leaf gas exchange, chlorophyll a fluorescence, and leaf reflectance parameters were measured in six-day frequency, while the leaf anatomy was examined only at the end of the second drought cycle. In both drought events, the WS plants showed reduction in stomatal conductance and leaf transpiration. The reduction in internal CO₂ diffusion was observed in the second drought cycle, expressed by increased thickness of spongy parenchyma in both clones. Those stomatal and anatomical traits impacted decreasing the A_net in both drought events. The ‘3V’ was less influenced by water stress than the ‘A1’ genotype in A_net, effective quantum yield in PSII photochemistry, photochemical quenching, linear electron transport rate, and photochemical reflectance index during the drought-1, but during the drought-2 event such an advantage disappeared. Such physiological genotype differences were supported by the medium xylem vessel area diminished only in ‘3V’ under WS. In both drought cycles, the recovery of all observed stomatal and non-stomatal responses was usually complete after 12 days of rewatering. The absence of photochemical impacts, namely in the maximum quantum yield of primary photochemical reactions, photosynthetic performance index, and density of reaction centers capable of Q_A reduction during the drought-2 event, might result from an acclimation response of the clones to WS. In the second drought cycle, the plants showed some improved responses to stress, suggesting “memory” effects as drought acclimation at a recurrent drought. Full article

The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model. Full article

This work presents a study of a ventilated hollow core slab system (VHCS) that obviates the need to completely replace the slab of an existing residential building. It is assimilated to a heat exchanger to allow its effectiveness to be studied as a function of the area and airflow rate. The balance between the energy consumed by the fan and the heat evacuated by the system is also studied through the use of the thermo-hydraulic performance factor (THPF), for which a series of cases were simulated by CFD following a methodology in which a configuration is achieved by means of the sequential analysis of cases in which both the thermal effectiveness and the THPF are maximized. The configuration chosen in this study was found to benefit from high airflow rates since, although this implies an increase in fan energy consumption, the increase in heat removed is proportionally greater. It has also been found that the design of the airflow distribution through the slab is of high importance as it affects both the heat exchanged with the slab and the pressure losses. An applicability map has been developed as a function of the temperature of the space below and the air temperature at the inlet of the ventilated roof. The heat flux per unit area that the studied envelope is able to evacuate is about 20 W/m² K. Full article

Contrasting the X-band phased array radar (XPAR) with the conventional S-Band dual-polarization mechanical scanning radar (SMSR), the XPAR offers superior temporal and spatial resolution, enabling a more refined depiction of the internal dynamics within convective systems. While both SMSR and XPAR data are extensively used in monitoring and alerting for severe convective weather, their comparative application in numerical weather prediction through data assimilation remains a relatively unexplored area. This study harnesses the Weather Research and Forecasting Model (WRF) and its data assimilation system (WRFDA) to integrate radial velocity and reflectivity from the Guangzhou SMSR and nine XPARs across Guangdong Province. Utilizing a three-dimensional variational approach at a 1 km convective-scale grid, the assimilated data are applied to forecast a rainstorm event in the Pearl River Delta (PRD) on 6 June 2022. Through a comparative analysis of the results from assimilating SMSR and XPAR data, it was observed that the assimilation of SMSR data led to more extensive adjustments in the lower- and middle-level wind fields compared to XPAR data assimilation. This resulted in an enlarged convergence area at lower levels, prompting an overdevelopment of convective systems and an excessive concentration of internal hydrometeor particles, which in turn led to spurious precipitation forecasts. However, the sequential assimilation of both SMSR and XPAR data effectively reduced the excessive adjustments in the wind fields that were evident when only SMSR data were used. This approach diminished the generation of false echoes and enhanced the precision of quantitative precipitation forecasts. Additionally, the lower spectral width of XPAR data indicates its superior detection accuracy. Assimilating XPAR data alone yields more reasonable adjustments to the low- to middle-level wind fields, leading to the formation of small-to-medium-scale horizontal convergence lines in the lower levels of the analysis field. This enhancement significantly improves the model’s forecasts of composite reflectivity and radar echoes, aligning them more closely with actual observations. Consequently, the Threat Score (TS) and Equitable Threat Score (ETS) for heavy-rain forecasts (>10 mm/h) over the next 5 h are markedly enhanced. This study underscores the necessity of incorporating XPAR data assimilation in numerical weather prediction practices and lays the groundwork for the future joint assimilation of SMSR and XPAR data. Full article

Before 2008, China lacked high-coverage regional surface observation data, making it difficult for the China Meteorological Administration Land Data Assimilation System (CLDAS) to directly backtrack high-resolution, high-quality land assimilation products. To address this issue, this paper proposes a deep learning model named UNET_DCA, based on the UNET architecture, which incorporates a Dual Cross-Attention module (DCA) for multiscale feature fusion by introducing Channel Cross-Attention (CCA) and Spatial Cross-Attention (SCA) mechanisms. This model focuses on the near-surface 10-m wind field and achieves spatial downscaling from 6.25 km to 1 km. We conducted training and validation using data from 2020–2021, tested with data from 2019, and performed ablation experiments to validate the effectiveness of each module. We compared the results with traditional bilinear interpolation methods and the SNCA-CLDASSD model. The experimental results show that the UNET-based model outperforms SNCA-CLDASSD, indicating that the UNET-based model captures richer information in wind field downscaling compared to SNCA-CLDASSD, which relies on sequentially stacked CNN convolution modules. UNET_CCA and UNET_SCA, incorporating cross-attention mechanisms, outperform UNET without attention mechanisms. Furthermore, UNET_DCA, incorporating both Channel Cross-Attention and Spatial Cross-Attention mechanisms, outperforms UNET_CCA and UNET_SCA, which only incorporate one attention mechanism. UNET_DCA performs best on the RMSE, MAE, and COR metrics (0.40 m/s, 0.28 m/s, 0.93), while UNET_DCA_ars, incorporating more auxiliary information, performs best on the PSNR and SSIM metrics (29.006, 0.880). Evaluation across different methods indicates that the optimal model performs best in valleys, followed by mountains, and worst in plains; it performs worse during the day and better at night; and as wind speed levels increase, accuracy decreases. Overall, among various downscaling methods, UNET_DCA and UNET_DCA_ars effectively reconstruct the spatial details of wind fields, providing a deeper exploration for the inversion of high-resolution historical meteorological grid data. Full article