Search Results (4,391)

The efficiency of anaerobic digestion (AD) of lignocellulosic biomass is strongly determined by biomass yield, chemical composition, and bioavailability, all of which undergo substantial seasonal variation. However, integrated analyses linking these factors with AD performance, process kinetics, and energy-economic efficiency remain limited. This study aimed to evaluate the effect of seasonal variability in the chemical composition of Helianthus annuus biomass on AD efficiency from a technological and economic perspective. The novelty of this study lies in integrating seasonal changes in biomass composition with AD kinetics, CH₄ productivity per hectare, and CHP techno-economic performance to identify the optimal harvest window for Helianthus annuus. The experiments were conducted using biomass harvested from June to December. The results showed significant (p < 0.05) variability in biomass properties, including a progressive increase in lignocellulosic fractions over the growing season, with neutral detergent fiber (NDF) increasing from 30.58 ± 1.8 to 66.58 ± 3.1% TS and acid detergent lignin (ADL) from 5.13 ± 0.5 to 10.35 ± 0.9% TS, accompanied by a decline in substrate bioavailability. The maximum CH₄ yield of 258 ± 13 mL/g VS was obtained in August, with a process rate of 29.0 ± 3.4 mL/g VS·d and the highest utilization of methane potential, reaching 62.5 ± 3.8% (BMP_CH4/TBMP). Correlation and regression analyses indicated that ADL and NDF were the strongest empirical predictors of AD performance within the analyzed dataset, showing a negative association with both CH₄ production yield and kinetics (R² up to 0.86), whereas reducing sugars had a stimulatory effect. Multiple regression models showed high predictive performance, with R² = 0.889 for BMP_CH4. The highest energy and economic efficiency was achieved in summer. In August, CH₄ production reached 3214 ± 596 m³/ha, corresponding to 11.2 ± 2.1 MWh/ha of electricity and a net result of 1559 ± 417 EUR/ha. Increased lignification in the later part of the season led to reduced process efficiency and a deterioration of the economic balance. From a practical perspective, these results demonstrate that harvest scheduling should be based on the trade-off between biomass quantity and biodegradability rather than on biomass yield alone. Full article

Phycobiliproteins (PBPs) are a family of pigment-proteins renowned for their exceptional light-harvesting, fluorescent, and antioxidant properties. Among cyanobacteria, Spirulina stands out as one of the richest natural sources of PBPs, particularly phycocyanin (PC) and allophycocyanin (APC), yet the large-scale production of analytical-grade PBPs remains hampered by an inherently complex downstream process that relies on multiple purification steps, compromising both yield and scalability. This work presents a streamlined strategy to obtain analytical-grade PC, combining ultrasound-assisted extraction (UAE) with an aqueous ionic liquid (IL) solution and a single hydrophobic interaction chromatography (HIC) step, integrated within a biorefinery framework. The proposed approach yielded analytical-grade PC with a recovery of up to 50.44% and enhanced APC purity up to 10.57-fold. Furthermore, the IL was successfully reused in both extraction and purification steps without compromising yield or purity. The environmental performance of the proposed process was assessed through a cradle-to-gate life cycle assessment (LCA), with system boundaries encompassing the following biorefinery stages: cultivation, harvesting and drying, PC extraction and purification, post-processing, and spent biomass valorization via anaerobic digestion. The LCA identified the main environmental hotspots and guided the proposal of targeted process improvements—particularly HIC salt substitution and increased IL recovery—which reduced environmental impacts by 65.9–89.8% across most categories. The proposed strategy was further benchmarked against two model scenarios for analytical-grade PC production, one conventional and one innovative, revealing its relative advantages and limitations. Overall, this work demonstrates a viable pathway for producing high-purity PC that balances process efficiency with environmental sustainability, supporting the development of greener microalgae-based bioprocesses. Full article

Light is a critical environmental cue regulating development and quality in edible fungi, yet the effects of dynamic light regimes (for example, transitions from white to blue light) remain poorly understood. We systematically investigated how white-light pretreatment duration (0, 4, 8, or 12 h) and two blue-light regimes—B6 (6 h blue followed by white until harvest) and Bc (continuous blue until harvest)—affect fruiting-body development, yield, color, textural properties, and nutritional quality of Pleurotus pulmonarius. The experiment was conducted at a single commercial production facility in Zhejiang Province, China, using the commercial strain P. pulmonarius (cultivar ‘Jinxiu’). Two-way ANOVA revealed significant interactions between white-light pretreatment and blue-light regime for cap a* value (red-green), cap width, cap hardness and chewiness, stipe hardness, number of fruiting bodies, and several nutrient components. All dynamic light regimes reduced cap L* value (lightness) and b* value (yellow-blue); continuous blue (Bc) produced a darker cap. Yield responses to blue-light duration depended on pretreatment: without white pretreatment, Bc outperformed B6, whereas with 4–12 h white pretreatment B6 produced higher yields. Relative to the control (CK), all dynamic regimes significantly increased total free amino acids and essential amino acids. Except for W4B6 and W12B6, all other treatments significantly increased crude protein; total soluble sugar, crude fat, and crude fiber decreased in most treatments compared to CK. These results indicate that an optimized transition from white to blue light can synergistically improve the color, nutritional quality and yield of P. pulmonarius. The W8Bc regime (8 h white pretreatment followed by continuous blue until harvest) produced the highest cap chewiness (21.65 N·mm) and free amino acid content (3110.44 μg·g⁻¹), the darkest cap color, and the top comprehensive score in the entropy-weighted TOPSIS evaluation, despite ranking second in yield and high-quality rate. Under the conditions tested (single cultivar ‘Jinxiu’ at one production base), we recommend the W8Bc light regime as suitable for industrial cultivation of Pleurotus pulmonarius. However, it should be noted that these findings cannot be generalized to the entire species without further validation across multiple strains and multiple locations. Full article

Salvia fruticosa Mill. (Anatolian sage) is an important medicinal and aromatic plant widely valued for its essential oil and phenolic compounds. Harvesting practices may influence both biomass yield and the chemical quality of plant raw materials. This study evaluated the effects of manual and machine harvesting on selected physical characteristics, essential oil composition, mineral content, and antioxidant-related phytochemical properties of S. fruticosa cultivated under Central Anatolian conditions, together with the operational performance of both harvesting methods. Manual harvesting resulted in higher fresh and dry biomass yields and a greater essential oil content (2.03%) compared with machine harvesting (1.57%). Mineral analysis showed that Ca, Zn, Cu, and B concentrations were higher in manually harvested samples, whereas K and Mg contents were slightly higher in machine-harvested plants. Essential oil characterization demonstrated that 1,8-cineole was the dominant compound and its proportion differed markedly between harvesting methods, reaching 43.07% in manual harvesting and 21.77% in machine harvesting. Antioxidant activity determined by the DPPH assay was 0.093 mg TE mL⁻¹ for manual harvesting and 0.096 mg TE mL⁻¹ for machine harvesting. Additional phytochemical analyses revealed total phenolic contents of 134.6 and 129.3 mg GAE g⁻¹ extract, total flavonoid contents of 22.7 and 25.2 mg QE g⁻¹ extract and FRAP values of 382 and 336 µmol Fe²⁺ g⁻¹ extract for manual and machine harvesting, respectively. These findings indicate that harvesting technique affects certain compositional parameters but causes only limited changes in the overall antioxidant potential of S. fruticosa extracts. Full article

Water limitations in the Brazilian semi-arid region require saline water utilization. Hydroponic cultivation combined with salicylic acid (SA) elicitation represents a strategy to manage salt stress in cherry tomatoes. This study evaluated the effects of foliar SA application on the production and quality of cherry tomatoes under saline nutrient solutions. An NFT hydroponic greenhouse experiment at UFCG, Pombal, Brazil, evaluated five nutrient solution salinities (ECns: 2.1, 2.6, 3.1, 3.6, and 4.1 dS m⁻¹) and five SA concentrations (0, 0.8, 1.6, 2.4, and 3.2 mM) in a split-plot design with three replications. SA concentrations from 1.3 to 3.2 mM enhanced fruit diameter, fruit number, average weight, and yield under baseline salinity (2.1 dS m⁻¹). At 3.2 mM, SA functioned as an optimal ratio regulating nutritional quality, increasing titratable acidity and ascorbic acid under 2.1 and 2.6 dS m⁻¹, respectively. Conversely, high salinity (4.1 dS m⁻¹) established a promotion pattern on soluble solids, maturity index, and flavonoids, while reducing yield components by up to 58.3%, demonstrating explicit operational limitations of SA under severe stress. These baseline findings validate the applicability of SA within specific salinity thresholds, establishing a foundational framework for subsequent physiological profiling, fruit quality characterization at harvest, and commercial greenhouse upscale validation. Full article

Olive (Olea europaea L.) orchards on the hyper-arid Peruvian coast (Tacna, ${18}^{\circ }$ S) suffered >70$\%$ yield collapses in the 2016 and 2024 El Niño seasons against a non-failure mean of $\sim 6$ t ha⁻¹ and a 2022 La Niña bumper harvest, raising the question of whether insufficient winter chilling is the binding climate constraint. We combined in situ daily meteorology (2015–2025) with yield records from eleven Sevillana–Ascolana parcels (88 parcel-years over eight seasons) and fitted a year-level log-OLS model with mean chill-window and fruit-growth temperatures, validated by year-block bootstrap, permutation, a closed-form Bayesian posterior, and a parcel-year mixed model. The model achieves $R_{log}^2=0.65$, and the chill slope ($\beta =-0.82$) is robust across three independent tests: one-sided permutation $p=0.036$; Bayesian posterior with 99.8% of mass below zero (Savage–Dickey BF₁₀ = 15.9); parcel-year mixed model $p<{10}^{-14}$. Counterfactual restoration of chill-window temperature to its non-failure climatology recovers the full collapse in both years, whereas restoring fruit-growth temperature recovers nothing. CMIP6 delta-method projections identify a chill-collapse threshold at $\Delta T_{\mathrm{winter}}\approx +{1.25 }^{\circ }$C; SSP1-2.6 alone reduces mid-century mean yield by $\sim 52\%$, and SSP5-8.5 reaches $-89\%$ by 2051–2070. Tacna emerges as a chill-sentinel system where winter warmth, not summer heat, is the binding constraint and the transition to the failure regime lies on a near-term adaptation horizon. Full article

This paper investigates the optimal harvesting of a nonlinear, size-structured population governed by a first-order transport equation with nonlocal environmental crowding feedback and exogenous inflow. First, we establish finite-horizon well-posedness for the controlled state system in an $L^1$ framework, proving the existence, uniqueness, positivity, and continuous dependence of weak solutions. Second, we show that the infinite-dimensional stationary problem reduces exactly to a scalar nonlinear closure equation, yielding existence and conditional uniqueness results for stationary states. Within this equilibrium framework, we distinguish the persistence of the forced system from intrinsic demographic self-replacement and introduce size-continuous per-recruit and spawning-potential diagnostics. Finally, we formulate a partial differential equation (PDE)-constrained optimal harvesting problem. Under a compactness assumption on the control-to-state map, we establish the existence of optimal controls. We then formally derive a Pontryagin-type first-order optimality system for the harvesting problem. The variation of the nonlocal environmental feedback produces a coupled integral source term in the adjoint equation. The associated pointwise maximization condition yields a bang–bang harvesting structure, while a monotone size-threshold policy is shown to require an additional single-crossing assumption on the switching function. These hypotheses are illustrated using a fisheries model with density-dependent von Bertalanffy growth. Full article

Essential oils from aromatic plants are gaining traction as naturally derived preservative and antioxidant ingredients, yet the quantitative relationships between field climate conditions and both oil yield and food relevant bioactivity remain poorly characterised. Here, we characterised the leaf essential oil of Melaleuca bracteata F. Muell. “Revolution Gold” across a complete annual cycle using a fixed plant, multiscale spatio temporal sampling framework. Leaf samples were collected at four seasonal time points (March, June, September, and December) and four diurnal time points (06:00, 12:00, 16:00, and 21:00) from a single field individual in Meizhou, Guangdong, China. Gas chromatography–mass spectrometry (GC-MS) profiling identified 51 volatile constituents, with methyl eugenol dominating the composition (up to 93.67% in summer). Oil yield peaked in summer (2.43 mL kg⁻¹ dry weight) and was lowest in spring (1.28 mL kg⁻¹ dry weight). DPPH radical scavenging and ABTS radical cation decolorisation assays revealed that antioxidant activity was highest in summer harvested oils, with IC₅₀ values of 7.68 mg mL⁻¹ (DPPH) and 8.85 mg mL⁻¹ (ABTS), consistent with peak methyl eugenol accumulation. Permutation-based multiple regression (999 permutations; R² = 0.947) identified seasonal precipitation as the strongest positive predictor of oil yield (β = 11.22, p < 0.05), while temperature exerted a significant negative influence (β = −11.21, p < 0.05). Non-metric multidimensional scaling (NMDS) and permutational multivariate analysis of variance (PERMANOVA) confirmed highly significant seasonal clustering of compositional profiles (F = 15.258, p = 0.001) against negligible diurnal structuring (F = 0.178, p =0.991). Redundancy analysis (RDA) attributed 71.03% of total compositional variance to the climatic predictor set. Pearson correlation analysis established significant positive associations between methyl eugenol content and antioxidant capacity (r > 0.80, p < 0.05). These findings provide an integrated, climate resolved basis for harvest timing optimisation of M. bracteata and identify summer as the strategically optimal harvest window for yield and bioactive functionality. Full article

Coriandrum sativum L. is a widely cultivated aromatic herb exhibiting substantial variation in essential oil quality and yield among different accessions. This study assessed germination performance, essential oil composition, yield, chemotype distribution, and fragrance characteristics in 48 C. sativum accessions collected from 19 countries spanning four Köppen–Geiger climate zones: Tropical/Subtropical, Arid/Semi-arid, Temperate, and Continental/Cold. All accessions were grown under standardized field conditions, and essential oils were extracted from aerial parts using steam distillation followed by direct-GC/MS analysis. Seed germination rates were consistently high (mean: 92.25 ± 5.85%; range: 71–100%) and did not differ significantly by climate zone (Kruskal–Wallis H = 5.500, p = 0.139) or country of origin (H = 21.833, p = 0.240), indicating that post-harvest management, rather than climatic provenance, primarily determines seed viability. Essential oil profiles were dominated by (E)-2-decenal (mean: 44.56%), decanal (11.75%), and 2-dodecenal (13.47%). Principal component analysis (PCA) of 18 compounds detected in at least 19 accessions accounted for 70.16% of total variance across five components, with PC1 reflecting a gradient from long-chain saturated aldehyde accumulation to linalool enrichment. Permutational multivariate analysis of variance (PERMANOVA) demonstrated significant compositional differentiation among climate zones (Pseudo-F = 1.662, p = 0.028), whereas country-level grouping was not significant (p = 0.256). Tropical/subtropical accessions exhibited the highest linalool content (mean: 15.39 ± 8.71%) and essential oil yield (mean: 0.269 ± 0.120% v/w), significantly surpassing arid/semi-arid and temperate zones (p < 0.05). Two chemotypes were identified, (E)-2-decenal (91.7%) and linalool (8.3%), each associated with distinct fragrance profiles (earthy/aldehydic/woody versus herbal/sweet, respectively). These findings demonstrate that climate zone of origin is significantly associated with C. sativum essential oil composition and productivity, with tropical/subtropical accessions providing superior yield and linalool content. Chemotype characterization offers an additional criterion for germplasm selection in targeted industrial applications. Full article

Accurate vineyard yield estimation is essential for harvest planning, resource allocation, and economic decision-making, particularly under conditions of high spatial variability. Traditional sampling-based methods are labor-intensive, destructive, and prone to error, especially in high-productivity free-canopy systems. This study developed and evaluated predictive models for commercial irrigated vineyards of Carménère and Chardonnay in Chile’s Maule Region across two growing seasons (2023–2025). Structural yield components, physiological measurements, and UAV-derived multispectral indices (NDVI, GNDVI, NDRE) were collected from georeferenced sampling grids. Modeling approaches included linear regression, stepwise selection, and machine learning algorithms (Random Forest, Multilayer Perceptron). Validation results showed that cluster number was the primary driver of yield variability, explaining up to 40% of variation. Incorporating physiological and spectral variables improved accuracy, with the best models (least squares and MLP) achieving R² values up to 0.66 and reducing errors to 12–15%. Spatial yield maps reproduced intra-vineyard variability patterns, demonstrating that integrating plant-level and canopy-level data substantially enhances yield prediction. These findings provide a robust framework for precision viticulture applications. Full article

Mungbean is an important legume crop native to India. In this study, 500 indigenous mungbean accessions collected from diverse eco-geographical regions of India were evaluated for agronomic trait genetic variability and core collection development. The accessions were grown in an augmented design during 2019 and 2020, and data were recorded for seven quantitative and 13 qualitative traits. Analysis of variance (ANOVA), frequency distribution, and box-plot analyses revealed substantial phenotypic variation among the accessions. Traits including plant height (PHT), number of pods per plant (NPP), hundred-seed weight (HSW), and single-plant yield (SPY) exhibited high heritability coupled with high genetic advance, indicating the predominance of additive genetic effects. Principal component analysis showed that the first three principal components explained 70% of the total phenotypic variation. The Shannon–Weaver diversity index further indicated high levels of genetic diversity within the population. Based on quantitative traits, the accessions were grouped into six major clusters and 42 sub-clusters, with SPY, NPP, HSW, PHT, and days to 50% flowering (DFF) contributing substantially to genetic divergence. Correlation analysis suggested that direct selection for SPY and indirect selection through associated traits, including NPP, HSW, PHT, NSP, and pod length (POL), may enhance yield improvement. The germplasm collection also possessed desirable traits such as high yield potential, contrasting maturity groups, and plant types suitable for mechanical harvesting and bold-seeded type. A representative core set comprising 50 accessions was developed using the PowerCore program, providing valuable genetic resources for mungbean breeding and genetic improvement programs. Full article

Biflavonoids are dimeric flavonoids recognized for their diverse biological activities and significant pharmacological potential, with ginkgo (Ginkgo biloba L.) serving as a primary natural source. This study presents a comprehensive spatiotemporal characterization of the biflavonoid profile across a diverse population of 90 trees. High-resolution chromatographic analysis quantified five major biflavonoids, revealing a consistent hierarchical abundance: sciadopitysin > isoginkgetin > ginkgetin > bilobetin > amentoflavone. Notably, sciadopitysin emerged as the predominant constituent (1532.89 ± 544.13 µg/g dw). To decode the complex drivers of metabolite accumulation, we integrated Principal Component Analysis (PCA) with Piecewise Linear Regression (PLR). PCA confirmed a robust chemical structure, explaining 71.5% of the total variance, where Factor 1 represents a general biflavonoid gradient and Factor 2 captures localized environmental influences. The PLR models (R² = 0.75–0.83) identified tree age as a primary negative regulator, showing a significant decline in total biflavonoids as trees mature beyond the 30-year reproductive threshold. While sexual dimorphism and location exhibited compound-specific nonlinear effects, younger trees (10–30 years) demonstrated the highest biosynthetic plasticity and potency. These findings establish a predictive framework for optimizing the pharmaceutical harvest of ginkgo leaves, highlighting that age-related physiological shifts, rather than gender or broad geography, are the critical determinants of biflavonoids yield. Full article

Background: Ivor Lewis esophagectomy for esophageal malignancies is a core element of the treatment but still comes with substantial postoperative complications. Especially within the learning curve of robotic esophagectomy, an optimal treatment pathway is crucial to minimize the clinical impact of this phase. This study aimed to compare the surgical outcomes of hybrid-robotic esophagectomy for adenocarcinoma of the esophagus with non-robotic procedures using a fail-safe protocol. Methods: This retrospective single-center study evaluated the outcome of hybrid-robotic procedures within the early robotic learning curve in comparison to a historical control group of laparoscopic and open non-robotic procedures using uni- and multivariable regression analysis. CUSUM analysis was applied for learning curve outcomes. All procedures were performed between January 2016 and December 2025 within a standardized fail-safe approach. The primary end point was the occurrence of anastomotic leakage (AL), whereas the overall complications as well as the number of harvested lymph nodes were secondary end points. Results: A total of 156 patients were analyzed, including 50 hybrid-robotic and 106 non-robotic resections. Robotic surgery was associated with a higher lymph node yield (40.9 ± 11.0 vs. 35.7 ± 13.1; p = 0.011) and a lower lymph node ratio (0.085 vs. 0.125; p = 0.046). The AL rate was 4.0% in the robotic group and 5.7% in the non-robotic group. Length of stay was significantly shorter after robotic procedures (13.1 ± 5.5 vs. 18.6 ± 11.3 days; p < 0.001). Conclusions: Establishing a robotic esophagectomy program within an established structured fail-safe protocol was not associated with an increased risk of anastomotic leakage or major complications. Within this standardized setting, an increased lymph node yield, potentially optimizing oncological quality was observed. These findings indicate that acceptable preoperative safety and oncological outcomes can be achieved even during the learning curve of hybrid-robotic esophagectomy embedded in a structured fail-safe protocol. Full article

Liquid-state thermocells (LTCs) are emerging electrochemical heat engines for harvesting low-grade thermal energy across small temperature differences. Their practical performance is jointly limited by internal dissipation associated with ionic and electrochemical transport, as well as by external irreversibility arising from finite thermal coupling to the heat source and sink. In this work, a finite-rate thermodynamic framework is developed for LTCs subject to coupled internal and external irreversibilities. The model combines effective thermoelectrochemical transport, a phenomenological asymmetric Joule-heat partition parameter motivated by electrode and interfacial heat effects, and non-ideal thermal contacts, thereby enabling analytical optimization of power output in four representative configurations. Closed-form expressions are derived for the maximum power and the efficiency at maximum power (EMP), together with the admissible operating domain and an equivalent-circuit interpretation. The results show that the thermal impedance ratio governs a transition between externally limited and internally limited regimes. In the externally dominated limit, all configurations recover the Curzon–Ahlborn efficiency, whereas in the internally dominated limit, the asymptotic EMP depends on the side receiving irreversible heat release. When both dominant irreversibilities are located on the hot side, the highest EMP is achieved, while the opposite configuration yields the lowest EMP. These findings provide a thermodynamic benchmark for the LTC architecture and clarify how thermal contact asymmetry and internal heat release pathways should be coordinated to enhance performance in low-grade heat recovery. Full article

Timely and accurate assessment of rice productivity, encompassing grain yield, aboveground biomass (AGB), and harvest index (HI), is essential for harvest planning, supply chain coordination, and food security. This study evaluates the feasibility of predicting all three productivity components using satellite and weather time series data while examining trade-offs between forecast accuracy and operational lead time. Five machine learning models (CatBoost, Gaussian Process Regression (GPR), Random Forest, Ridge regression, and TabPFN) were compared across six in-season prediction windows (December to May) using Sentinel-2 vegetation indices (Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red Edge 2 (CIRE2), Land Surface Water Index (LSWI)), weather variables (minimum and maximum temperature and radiation), and agronomic records from 256 commercial and experimental rice fields in southern New South Wales, Australia, over four growing seasons (2022–2025) using leave-one-year-out cross-validation. Rolling in-season forecasts were evaluated across December–May; March was selected for further analysis as a practical window that balances accuracy and timeliness for decision-making, with minimal additional error reduction in later months closer to harvest. TabPFN had the lowest RMSE for yield prediction (RMSE = 1.85 t ha⁻¹, $r=0.72$), Ridge had the lowest RMSE for AGB (RMSE = 3.05 t ha⁻¹, $r=0.77$), while tree-based models yielded the lowest RMSE for derived HI (RMSE ≈ 0.07). HI prediction showed weak regional relationships, with direct prediction yielding $\left|r\right|\approx 0.24$ and derived HI (predicted yield divided by predicted AGB) showing $r\approx 0$. Although strong correlations ($r>0.9$) between HI and vegetation indices were observed within individual site-seasons, consistent with other studies, these relationships were highly variable across site-seasons, reflecting the difficulty of inferring HI from canopy reflectance when biotic and/or abiotic stresses decouple AGB accumulation from grain filling. Both direct and derived HI approaches yielded comparable errors, indicating that satellite and weather data lack information content for regional-scale HI prediction. These findings support satellite-based yield and AGB forecasting for operational use. Full article