Search Results (44)

Germplasm resources embody the genetic diversity of plants and form the foundation for breeding and the ongoing improvement of elite cultivars. The establishment of germplasm banks, along with their systematic evaluation, constitutes a critical step toward the conservation, sustainable use, and innovative utilization of these resources. Liriodendron, a rare and endangered tree genus with species distributed in both East Asia and North America, holds considerable ecological, ornamental, and economic significance. However, a standardized evaluation system for Liriodendron germplasm remains unavailable. In this study, 297 Liriodendron germplasm accessions were comprehensively evaluated using 34 phenotypic traits and whole-genome resequencing data. Substantial variation was observed in most phenotypic traits, with significant correlations identified among several characteristics. Cluster analysis based on phenotypic data grouped the accessions into three distinct clusters, each exhibiting unique distribution patterns. This classification was further supported by principal component analysis (PCA), which effectively captured the underlying variation among accessions. These phenotypic groupings demonstrated high consistency with subsequent population structure analysis based on SNP markers (K = 3). Notably, several key traits exhibited significant divergence (p < 0.05) among distinct genetic clusters, thereby validating the coordinated association between phenotypic variation and molecular markers. Genetic diversity and population structure were assessed using 4204 high-quality single-nucleotide polymorphism (SNP) markers obtained through stringent filtering. The results indicated that the Liriodendron sino-americanum displayed the highest genetic diversity, with an expected heterozygosity (He) of 0.18 and a polymorphic information content (PIC) of 0.14. In addition, both hierarchical clustering and PCA revealed clear population differentiation among the accessions. Association analysis between three phenotypic traits (DBH, annual height increment, and branch number) and SNPs identified 25 highly significant SNP loci (p < 0.01). Of particular interest, the branch number-associated locus SNP_17_69375264 (p = 1.03 × 10⁻⁵) demonstrated the strongest association, highlighting distinct genetic regulation patterns among different growth traits. A minimal set of 13 core SNP markers was subsequently used to construct unique DNA fingerprints for all 297 accessions. In conclusion, this study systematically characterized phenotypic traits in Liriodendron, identified high-quality and core SNPs, and established correlations between key phenotypic and molecular markers. These achievements enabled differential analysis and genetic diversity assessment of Liriodendron germplasm, along with the construction of DNA fingerprint profiles. The results provide crucial theoretical basis and technical support for germplasm conservation, accurate identification, and utilization of Liriodendron resources, while offering significant practical value for variety selection, reproduction and commercial applications of this species. Full article

In the era of information explosion, recommendation systems play a crucial role in filtering vast amounts of content for users. Traditional recommendation models leverage knowledge graphs, sentiment analysis, social capital, and generative AI to enhance personalization. However, existing models still struggle to adapt dynamically to users’ evolving interests across multiple content domains in real-time. To address this gap, the cross-domain adaptive recommendation system (CDARS) is proposed, which integrates real-time behavioral tracking with multi-domain knowledge graphs to refine user preference modeling continuously. Unlike conventional methods that rely on static or historical data, CDARS dynamically adjusts its recommendation strategies based on contextual factors such as real-time engagement, sentiment fluctuations, and implicit preference drifts. Furthermore, a novel explainable adaptive learning (EAL) module was introduced, providing transparent insights into recommendations’ evolving nature, thereby improving user trust and system interpretability. To enable such real-time adaptability, CDARS incorporates multimodal sentiment analysis of user-generated content, behavioral pattern mining (e.g., click timing, revisit frequency), and learning trajectory modeling through time-aware embeddings and incremental updates of user representations. These dynamic signals are mapped into evolving knowledge graphs, forming continuously updated learning charts that drive more context-aware and emotionally intelligent recommendations. Our experimental results on datasets spanning social media, e-commerce, and entertainment domains demonstrate that CDARS significantly enhances recommendation relevance, achieving an average improvement of 7.8% in click-through rate (CTR) and 8.3% in user engagement compared to state-of-the-art models. This research presents a paradigm shift toward truly dynamic and explainable recommendation systems, creating a way for more personalized and user-centric experiences in the digital landscape. Full article

The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of vegetation and soil moisture in semiarid rangelands using vegetation and soil moisture indices. We aim to study the feasibility of using soil moisture negative anomalies as a warning index for vegetation or agricultural drought. Two semiarid agricultural regions were selected in Spain for this study: Los Vélez (Almería) and Bajo Aragón (Teruel). MODIS images, with 250 m and 500 m spatial resolution, from 2002 to 2019, were acquired to calculate the Vegetation Condition Index (VCI) and the Water Condition Index (WCI) based on the Normalised Difference Vegetation Index (NDVI) and soil moisture component (W), respectively. The Optical Trapezoid Model (OPTRAM) estimated this latter W index. From them, the anomaly (Z-score) for each index was calculated, being $Z_{VCI}$ and $Z_{WCI}$, respectively. The probability of coincidence of their negative anomalies was calculated every 10 days (10-day periods). The results show that for specific months, the $Z_{WCI}$ had a strong probability of informing in advance, where the negative $Z_{VCI}$ will decrease. Soil moisture content and vegetation indices show more similar dynamics in the months with lower temperatures (from autumn to spring). In these months, given the low temperatures, precipitation leads to vegetation growth. In the following months, water availability depends on evapotranspiration and vegetation type as the temperature rises and the precipitation falls. The stronger relationship between vegetation and precipitation from autumn to the beginning of spring is reflected in the feasibility of $Z_{WCI}$ to aid the prediction of $Z_{VCI}$. During these months, using $Z_{WCI}$ as a warning index is possible for both areas studied. Notably, November to the beginning of February showed an average increase of 20–30% in the predictability of vegetation anomalies, knowing moisture soil anomalies four lags in advance. We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón. Full article

Ullucus tuberosus is an Andean region crop adapted to high-altitude environments and dryland cultivation. It is an essential resource that guarantees food security due to its carbohydrate, protein, and low-fat content. However, current change patterns in precipitation and temperatures warn of complex scenarios where climate change will affect this crop. Therefore, predicting these effects through simulation is a valuable tool for evaluating this crop’s sustainability. This study aims to evaluate ulluco’s crop yield under dryland conditions at 3914 m.a.s.l. considering climate change scenarios from 2024 to 2100 by using the AquaCrop model. Simulations were carried out using current meteorological data, crop agronomic information, and simulations for SSP1-2.6, SSP3-7.0, and SSP5-8.5 of CMIP 6. The results indicate that minimum temperature increases and seasonal precipitation exacerbation will significantly influence yields. Increases in rainfall and environmental CO₂ concentrations show an opportunity window for yield increment in the early stages. However, a negative trend is observed for 2050–2100, mainly due to crop temperature stress. These findings highlight the importance of developing more resistant ulluco varieties to heat stress conditions, adapting water management practices, continuing modeling climate change effects on crops, and investing in research on smallholder agriculture to reach Sustainable Development Goals 1, 2, and 13. Full article

Deep coalbed methane (DCBM) reservoirs hold exceptional potential for diversifying energy sources. The Ordos Basin has attracted much attention due to its enormous resource reserves of DCBM. This work focuses on the Jiaxian area of the Ordos basin, and the multi-factor quantitative evaluation method on the sealing of cap rocks is established. The abundant geologic and reservoir information is synthesized to explore variable factors affecting the gas content. Results indicate that the sealing capacity of the coal seam roof in the Jiaxian area, with a mean sealing index of 3.12, surpasses the floor’s sealing capacity by 13.87%, which averages 2.74. The sealing of the coal seam roof has a more positive impact on the enrichment of coalbed methane (CBM). In addition, the conditions for preserving gas would be boosted as coal seam thickness increased, leading to enhanced gas content in coal seams. The CH₄ content increases by an average of ~2.38 m³/t as coal seam thickness increases with the interval of 1 m. The increasing burial depth represents the incremental maturity of organic matter and the gas generation ability in coal seams, which contributes to improving the gas content in coal seams. There is a positive correlation between the degree of coal fragmentation and the gas content of the coal seam to a certain extent. These findings provide valuable insights for targeted drilling strategies and enhancing natural gas production capacity in the Jiaxian area of the Ordos Basin. Full article

We demonstrate how many important measures of belief about the environmental suffer from poor content validity and inadequate conceptual breadth (dimensionality). We used scholarship in environmental science and philosophy to propose a list of 13 environmental concepts that can be held as beliefs. After precisely articulating the concepts, we developed 85 trial survey items that emphasized content validity for each concept. The concepts’ breadth and the items’ content validity were aided by scrutiny from 17 knowledgeable critics. We administered the trial items to 449 residents of the United States and used item response theory to reduce the 85 trial items to smaller sets of items for use when survey brevity is required. The reduced sets offered good predictive ability for two environmental attitudes (R² = 0.42 and 0.46) and indices of pro-environmental behavior (PEB, R² = 0.23) and behavioral intention (R² = 0.25). The predictive results were highly interpretable, owing to their robust content validity. For example, PEB was predicted by the degree to which one believes nature to be sacred, but not by the degree of one’s non-anthropocentrism. Concepts with the greatest overall predictive ability were Sacredness and Hope. Belief in non-anthropocentrism had little predictive ability for all four response variables—a claim that previously could not have been made given the widespread poverty of content validity for items representing non-anthropocentrism in existing instruments. The approach described here is especially amenable to incremental improvement, as other researchers propose more informative survey items and potentially important concepts of environmental beliefs we overlooked. Full article

With the development of industry and agriculture, polycyclic aromatic hydrocarbons (PAHs) in the agricultural sector have gradually increased to different degrees, leading to an escalation in environmental pollution. In turn, this escalation has presented a significant possibility of endangering agricultural practices on farmland and has had a serious impact on regional sustainable development. Therefore, a total of 117 samples of soil were gathered to research the pollution level, distribution, sources, and health risk of PAHs in Helan farmland soils. A reference was used for the identification and quantification of PAH content using high-performance liquid chromatography (HPLC) with an ultraviolet detector, and their spatial distribution was analyzed utilizing the Arc Geographic Information System (ArcGIS). The source of PAHs was analyzed by absolute principal component scores/multiple linear regression (APCS-MLR). The lifetime cancer risk increment model and Monte Carlo sensitivity analysis were used to assess the potential health hazards to humans associated with PAHs in soil. Within the current study area, PAHs were higher in the northwest. The results showed that the total content of PAHs in Helan farmland soil ranged from 17.82 to 1544.73 ng·g⁻¹ with a mean of 408.18 ng·g⁻¹, which indicated the middle degree of pollution in farmland soil. The verification results of the APCS-MLR model showed that the correlation coefficient between the measured values and the predicted values ranged from 0.661 to 0.984, which suggested that the APCS-MLR model demonstrated favorable suitability for conducting source analysis of PAHs in the soil within the study region. Based on the contribution of PAHs from each source, the main sources of PAHs in Helan farmland soil were the combustion source (biomass, diesel, and natural gas combustion) and the transportation source (gasoline for vehicles and traffic exhaust emissions). The health risks’ estimation showed that PAHs in farmland soil did not have potential health risks for adults but represented a carcinogenic risk for children via the main exposure pathway of ingestion with the mean intake of 1.28 × 10⁻⁵. Meanwhile, the carcinogenic risks (CRs) of dermal contact for the mean value of adults (9.32 × 10⁻⁷) was found to be higher than that for children (3.18 × 10⁻⁸). From the Monte Carlo simulation, the soil particle uptake rate was the most sensitive to the health risks of children and adults with risk probabilities of 26% and 52%, and the risk probabilities from body weight were −11% and −1%, whose negative value indicated that the increase in body weight could reduce the health risks to human. These findings could provide reference for the study of soil organic pollution in Helan farmland soil and contribute significantly to the preservation of the ecological environment, maintaining human health and safety, and promoting the sustainable development of regional farmland. Full article

Rice (Oryza sativa L.) is an important cereal crop worldwide due to its long domestication history. North-Eastern India (NEI) is one of the origins of indica rice and contains various native landraces that can withstand climatic changes. The present study compared NEI rice landraces to a check variety for phenological, morpho-physiological, and yield-associated traits under high temperatures (HTs) and elevated CO₂ (eCO₂) levels using molecular markers. The first experiment tested 75 rice landraces for HT tolerance. Seven better-performing landraces and the check variety (N22) were evaluated for the above traits in bioreactors for two years (2019 and 2020) under control (T1) and two stress treatments [mild stress or T2 (eCO₂ 550 ppm + 4 °C more than ambient temperature) and severe stress or T3 (eCO₂ 750 ppm + 6 °C more than ambient temperature)]. The findings showed that moderate stress (T2) improved plant height (PH), leaf number (LN), leaf area (LA), spikelets panicle⁻¹ (S/P), thousand-grain weight (TGW), harvest index (HI), and grain production. HT and eCO₂ in T3 significantly decreased all genotypes’ metrics, including grain yield (GY). Pollen traits are strongly and positively associated with spikelet fertility at maturity and GY under stress conditions. Shoot biomass positively affected yield-associated traits including S/P, TGW, HI, and GY. This study recorded an average reduction of 8.09% GY across two seasons in response to the conditions simulated in T3. Overall, two landraces—Kohima special and Lisem—were found to be more responsive compared to other the landraces as well as N22 under stress conditions, with a higher yield and biomass increment. SCoT-marker-assisted genotyping amplified 77 alleles, 55 of which were polymorphic, with polymorphism information content (PIC) values from 0.22 to 0.67. The study reveals genetic variation among the rice lines and supports Kohima Special and Lisem’s close relationship. These two better-performing rice landraces are useful pre-breeding resources for future rice-breeding programs to increase stress tolerance, especially to HT and high eCO₂ levels under changing climatic situations. Full article

The information bottleneck (IB) framework formalises the essential requirement for efficient information processing systems to achieve an optimal balance between the complexity of their representation and the amount of information extracted about relevant features. However, since the representation complexity affordable by real-world systems may vary in time, the processing cost of updating the representations should also be taken into account. A crucial question is thus the extent to which adaptive systems can leverage the information content of already existing IB-optimal representations for producing new ones, which target the same relevant features but at a different granularity. We investigate the information-theoretic optimal limits of this process by studying and extending, within the IB framework, the notion of successive refinement, which describes the ideal situation where no information needs to be discarded for adapting an IB-optimal representation’s granularity. Thanks in particular to a new geometric characterisation, we analytically derive the successive refinability of some specific IB problems (for binary variables, for jointly Gaussian variables, and for the relevancy variable being a deterministic function of the source variable), and provide a linear-programming-based tool to numerically investigate, in the discrete case, the successive refinement of the IB. We then soften this notion into a quantification of the loss of information optimality induced by several-stage processing through an existing measure of unique information. Simple numerical experiments suggest that this quantity is typically low, though not entirely negligible. These results could have important implications for $\left(i\right)$ the structure and efficiency of incremental learning in biological and artificial agents, $\left(ii\right)$ the comparison of IB-optimal observation channels in statistical decision problems, and $\left(iii\right)$ the IB theory of deep neural networks. Full article

Understanding spatial and temporal variability in soil organic carbon (SOC) content helps simultaneously assess soil fertility and several parameters that are strongly associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration. Therefore, it appears necessary to monitor SOC regularly and investigate rapid, non-destructive, and cost-effective approaches for doing so, such as proximal and remote sensing. To increase the accuracy of predictions of SOC content, this study evaluated combining remote sensing time series with laboratory spectral measurements using machine and deep-learning algorithms. Partial least squares (PLS) regression, random forest (RF), and deep neural network (DNN) models were developed using Sentinel-2 (S2) time series of 58 sampling points of bare soil and according to three approaches. In the first approach, only S2 bands were used to calibrate and compare the performance of the models. In the second, S2 indices, Sentinel-1 (S1) indices, and S1 soil moisture were added separately during model calibration to evaluate their effects individually and then together. In the third, we added the laboratory indices incrementally and tested their influence on model accuracy. Using only S2 bands, the DNN model outperformed the PLS and RF models (ratio of performance to the interquartile distance RPIQ = 0.79, 1.36 and 1.67, respectively). Additional information improved performances only for model calibration, with S1 soil moisture yielding the most stable improvement among three iterations. Including equivalent indices of the S2 indices calculated using soil spectra obtained under laboratory conditions improved prediction of SOC, and the use of only two indices achieved good validation performances for the RF and DNN models (mean RPIQ = 2.01 and 1.77, respectively). Full article

Small changes in soil aggregates-associated organic carbon and soil nitrogen (N) can induce huge fluctuations in greenhouse gas emissions and soil fertility. However, there is a knowledge gap regarding the responses to long-term continuous rotation systems, especially in N-fixing and non-N-fixing crop wheat in terms of the distribution of soil aggregates and the storage of soil carbon (C) and N in aggregates in the semiarid calcareous soil of Central China. This information is critical for advancing knowledge on C and N sequestration of soil aggregates in rainfed crop rotation systems. Our aim was to determine which legume (soybean (Glycine max)– or mung bean (Vigna radiata)–wheat (Triticum aestivum) rotation practice is more conducive to the formation of good soil structure and C and N fixation. A 10-year field experiment, including a soybean (Glycine max)–winter wheat (Triticum aestivum) rotation (SWR) with yield increments of 2020 compared to 2010 achieving 18.28% (soybean) and 26.73% (wheat), respectively, and a mung bean (Vigna radiata)–winter wheat rotation (MWR) achieving 32.66% (mung bean) and 27.38% (wheat), as well as farmland fallow, was conducted in Henan Province, China. The soil organic carbon (SOC), N content in the soil, and the soil aggregates were investigated. Legume–wheat rotation cropping enhanced the proportion of the >2 mm soil fractions and reduced the <0.053 mm silt + clay in the 0–40 cm soil profile. In the 0–30 cm soil layer, the SWR had a greater increment of the >2 mm aggregate fractions than the MWR. Two legume–winter wheat rotations enhanced the C and N sequestration that varied with soil depths and size fractions of the aggregate. In contrast, the MWR had greater SOC stocks in all fractions of all sizes in the 0–40 cm soil layers. In addition, the greater storage of N in the macro-, micro-, and silt + clay fractions was observed in the 0–30 cm layers; the MWR enhanced the C/N ratios in most of the size aggregates compared with the SWR. The MWR cropping system is more beneficial to the formation of good soil structure and the increasement of C and N reserves in soil. Thus, these findings show that mung bean, in contrast with soybean in the legume–wheat rotation system of a semiarid temperate zone, may offer soil quality improvement. Full article

The aim of this study was to evaluate the concentrations of polycyclic aromatic hydrocarbons (PAHs) in 1168 groundwater samples of the Campania Plain (Southern Italy), taken using a municipal environmental pressure index (MIEP), and to analyze the distribution of these compounds to determine source PAHs using ratios of isomers diagnostic. Lastly, this study also aimed to estimate the potential health cancer risk in groundwaters. The data indicated that the highest concentration of PAHs was found in groundwater from Caserta Province and the contents of BghiP, Phe, and Nap were detected in the samples. The spatial distribution of these pollutants was evaluated using the Jenks method; moreover, the data indicated that incremental lifetime cancer risk ILCR_ingestion ranged from 7.31 × 10⁻²⁰ to 4.96 × 10⁻¹⁹, while ILCR_dermal ranged from 4.32 × 10⁻¹¹ to 2.93 × 10⁻¹⁰. These research findings may provide information about the Campania Plain’s groundwater quality and aid in the development of preventative measures to lessen PAH contamination in groundwater. Full article

The purpose of formal education is to increase students’ abilities, and its content is to impart knowledge through various courses. Thus, it is essential to accurately identify the relationship between knowledge and students’ ability increment to ensure the quality of education and the sustainable development of education. Currently, this relationship is mainly established based on previous educational data and teachers’ experience, which is often imprecise. This paper proposes a framework for knowledge and ability recognition based on the structural characteristics of complex network modules. The proposed framework utilizes a knowledge cognitive-interdependent network model (KCIN) as its object. First, the key knowledge nodes are identified via cognitive convergence flow of knowledge nodes in KCIN. Subsequently, the module structure of the knowledge network is identified by taking the key knowledge nodes as the core. Finally, the relationship between knowledge and ability is established by identifying the similar attributes of nodes in complex network modules. To validate the framework, we use teaching process data on the Data Structure course, which is a fundamental course for Information majors. The results show that the framework can effectively optimize the knowledge–ability relationship acquired from previous data and teacher experience. Full article

Biodegradable polymers have recently become popular; in particular, blends of poly(lactic acid) (PLA) and poly(butylene adipate terephthalate) (PBAT) have recently attracted significant attention due to their potential application in the packaging field. However, there is little information about the thermomechanical properties of these blends and especially the effect induced by the addition of PBAT on the shape memory properties of PLA. This work, therefore, aims at producing and investigating the microstructural, thermomechanical and shape memory properties of PLA/PBAT blends prepared by melt compounding. More specifically, PLA and PBAT were melt-blended in a wide range of relative concentrations (from 85/15 to 25/75 wt%). A microstructural investigation was carried out, evidencing the immiscibility and the low interfacial adhesion between the PLA and PBAT phases. The immiscibility was also confirmed by differential scanning calorimetry (DSC). A thermogravimetric analysis (TGA) revealed that the addition of PBAT slightly improved the thermal stability of PLA. The stiffness and strength of the blends decreased with the PBAT amount, while the elongation at break remained comparable to that of neat PLA up to a PBAT content of 45 wt%, while a significant increment in ductility was observed only for higher PBAT concentrations. The shape memory performance of PLA was impaired by the addition of PBAT, probably due to the low interfacial adhesion observed in the blends. These results constitute a basis for future research on these innovative biodegradable polymer blends, and their physical properties might be further enhanced by adding suitable compatibilizers. Full article

Using a multi-analytical approach, this paper aimed to investigate the effect of apple juice enrichment with Arbutus unedo and Diospyros kaki fruits, Myrtus communis berry extract, Acca sellowiana, or Crocus sativus flower by-products on both bioactive compounds content and antioxidant activity. Physico-chemical parameters, vitamin C, sugars, organic acids, total polyphenol content, antioxidant activity, and sensory attributes were evaluated. An LC-PDA/MS QTof analysis allowed for the identification of 80 different phenolic compounds. The highest polyphenol content (179.84 and 194.06 mg of GAE/100 g fw) and antioxidant activity (CUPRAC, 6.01 and 7.04 mmol of Fe²⁺/100 g fw) were observed in products with added A. sellowiana and D. kaki, respectively. Furthermore, the study showed a positive correlation between polymeric procyanidins and antioxidant activity (0.7646–0.8539). The addition of A. unedo fruits had a positively significant influence on the increment of vitamin C (23.68 ± 0.23 mg/100 g fw). The obtained products were attractive to consumers, especially those with 0.1% C. sativus flower juice, M. communis berry extract, and persimmon D. kaki fruits. The synergy among the different analytical techniques allowed us to obtain a complete set of information, demonstrating that the new apple smoothies were enriched in both different beneficial molecules for human health and in antioxidant activity. Full article