Search Results (3,270)

Mediterranean regions are forecasted to be increasingly threatened by climate change, leading to the occurrence of extreme events. One strategy to improve the resilience of agricultural systems is to introduce rotations that combine legumes and crops with high intraspecific diversity such as evolutionary populations (EPs). These cropping systems may be characterized by lower external input needs and higher buffering capacity than traditional ones. Our objective was to test if the introduction of wheat EPs impacts soil microbial functions—including microbial biomass, community structure, and enzymatic activity—and soil organic matter composition within a crop rotation framework. We conducted a two-year field experiment at two sites in Italy comparing a modern bread wheat variety to two EPs, evolved in different areas, in rotation with legumes. The composition and processes of rhizosphere microbial communities were characterized using EL-FAME and enzyme activities. In addition, rhizosphere soil organic matter signatures were measured by mid-infrared spectroscopy, and their relationships with microbial parameters were investigated using principal component analyses. The results showed that the EP–rhizosphere relationship, as well as its influence on microbial abundance and activity, is dependent both on the site of origin and local pedoclimatic conditions, although no consistent response was observed across the two sites. These effects may be buffered by the choice of the preceding crop in rotation. Full article

We, the reviewers, explore Alex Baratta’s The Societal Codification of Korean English, highlighting Korean English (KE), since expanding circle English varieties are often overlooked despite their significant global role. Baratta argues that codification should be reconceptualized as a societal process driven by users themselves, where socially-used innovations become legitimate conventions, rather than having to be officially recognized as per tradition. Building on and moving beyond other works, he insists the field cannot wait for formal codification, even while acknowledging that some may find his framing of KE unconvincing or premature. We summarize such arguments around the legitimization of KE, offer insights into what Baratta’s work effectively addresses and leaves less explored. We then offer a conceptual matrix and metaphor to depict the complexity of KE and its codification. Finally, we introduce a new term, “K-English(es)”. The review aims to help readers better grasp the nuanced dynamics of KE that Baratta and the field engage with and to situate readers’ own interests (e.g., as English language teachers or Hallyu fans) around KE, while supporting the expanding scholarship on the topic. Full article

Accurate monitoring of the leaf area index (LAI) throughout the entire growth cycle of walnut trees using UAV multispectral imagery is essential for digital orchard management. In this study, focusing on the ‘Wen 185’ walnut variety in Xinjiang, we simultaneously acquired UAV multispectral images and ground-measured LAI data during four critical growth stages: expansion, hard shell, oil conversion, and maturity. A total of 25 vegetation indices and 48 texture features derived from the gray-level co-occurrence matrix were extracted. Hybrid feature selection combining linear (Pearson correlation), nonlinear (maximum information coefficient and random forest importance), and multiple consensus strategies was employed to reduce redundancy. LAI prediction models were constructed using four algorithms: Random Forest (RF), Support Vector Machine (SVM), LASSO, and Ridge Regression (RR), with model interpretability enhanced by SHAP analysis. Results showed that the multiple consensus screening reduced feature redundancy by an average of 69.6%. SHAP identified five core features: Redge_750_Mean, NDVI, B_Mean, RENDVI, and G_Homogeneity. Importantly, predictor importance shifted significantly with phenology: texture features dominated during the expansion stage, while red-edge indices (RENDVI and Redge_750_Mean) became predominant during the hard shell and oil conversion stages, effectively mitigating the saturation problem commonly observed in traditional indices such as NDVI within the LAI range of 1.5–5.8 in this study. The hybrid feature subset combining “red-edge spectrum + spatial texture” with the Random Forest algorithm achieved superior performance across all stages, with the RPD value exceeding 2.0 during the oil conversion stage, indicating excellent estimation capability. This study demonstrates that a “quality over quantity” feature selection strategy not only reduces model complexity but also enables high-precision, dynamic LAI monitoring throughout the entire walnut growth cycle, providing a scientific basis for intelligent management of large-scale orchards in arid regions. Full article

Accurately solving high-dimensional partial differential equations (PDEs) remains a central challenge in computational mathematics. Traditional numerical methods, while effective in low-dimensional settings or on coarse grids, often struggle to deliver the precision required in practical applications. Recent machine learning-based approaches offer flexibility but frequently fall short in terms of accuracy and reliability, particularly in industrial contexts. In this work, we explore a quantum-inspired method based on quantized tensor trains (QTT), enabling efficient and accurate solutions to PDEs in a variety of challenging scenarios. Through several representative examples, we show that the QTT approach can achieve logarithmic scaling in memory and computational cost for linear PDEs when the relevant QTT ranks remain moderate. We also develop QTT space-time formulations that treat time as an additional dimension, allowing the full temporal evolution to be represented and solved globally rather than through sequential time stepping. For the nonlinear Burgers equation, we study both time-stepping and a frozen-coefficient space-time Picard scheme in QTT form, and report empirical convergence behavior on smooth one-dimensional viscous test problems. Additionally, we present a proof-of-concept data-driven workflow within the quantum-inspired framework, in which sampled source data are interpolated into QTT form and then incorporated directly into the structured PDE solver. Full article

Given the enormous span of potential strategies to address climate change, it is difficult to build consensus on what to prioritize. In 2021, we conducted 63 semi-structured interviews with climate change experts in the U.S. (N = 33) and India (N = 30). Experts indicated how they would address climate change through mitigation, adaptation, carbon dioxide removal (CDR), and solar geoengineering (SG). Our experts studied climate change from a variety of disciplines and were not necessarily subject matter experts in CDR or SG. Most experts stated that while more research is needed on CDR and SG, there is low appeal to deploying them in responding to climate change. Across our entire sample, we find that 44% of experts supported deploying CDR compared to 3% for SG. We also find that 17% of experts opposed the deployment of CDR, while twice as many (35%) opposed deploying SG. While there is far more support for traditional measures like mitigation and adaptation, most experts were hesitant to support technologies like CDR and SG to limit warming to 1.5 °C or 2 °C to prevent dangerous climate impacts, with statements tending toward a precautionary principle. Deep interdisciplinary engagement by climate change experts on CDR and SG is essential to understanding these technologies’ potential roles in addressing climate change and the perceptions of risk of these technologies held by experts who work on other areas of the climate problem. We highlight the potential for follow-up studies on broader expert opinions of CDR and SG, as well as evaluating whether perceptions and opinions are lagging behind fast-changing developments in the field. Full article

In Italy, the apple cider market is experiencing significant growth, driven by numerous small-scale artisanal producers who combine local apple varieties with traditional processes to offer complex, and diverse products. However, artisanal production based on spontaneous fermentations often encounters challenges in qualitative reproducibility, particularly related to sensory issues (stability across different vintages and high turbidity of the product). In this context, a methodology has been developed to optimize the technological process of cider production at Contrada Contro in the Monti Sibillini (MC), in Marche region, Italy. The research focused on the isolation and selection of indigenous yeasts from frozen must prepared in the 2023 vintage. Following isolation and preliminary characterization, the indigenous yeasts were used to referment the still cider, followed by 7 months of bottle aging, and a second sampling point was conducted after 14 months of aging on lees. Destructive analyses using HPLC-DAD and GC-MS were conducted to evaluate polyphenols and volatile compounds, while non-destructive analyses with a 12-quartz microbalance electronic nose and near infrared (NIR) spectroscopy allowed for a quicker assessment of production techniques. Chromatographic analysis results showed that the sensory quality of refermented products was strongly influenced by the composition of the yeast strains used. All fermentations inoculated with selected yeasts exhibited lower turbidity compared to spontaneous fermentation. These findings indicate that the selection of indigenous yeasts for cider refermentation enables the production of a high-quality product, enriched with beneficial compounds and characterized by a strong terroir identity, underscoring the importance of microbiological terroir. Full article

As plant-based diets catalyze a global shift toward sustainable consumption, Chinese plant-based food firms are experiencing rapid growth and seeking to expand their international footprint. This study investigates the mechanisms underlying the internationalization performance of these firms by integrating the Technology–Organization–Environment (TOE) framework with a configurational perspective. We operationalize nine antecedents across three dimensions: the technological dimension (technological maturity, supply chain resilience, and digital transformation), the organizational dimension (food safety certification intensity, strategic partnership intensity, and talent acquisition intensity), and the environmental dimension (market adaptability, compliance and risk management, and product line breadth). Utilizing fuzzy-set qualitative comparative analysis (fsQCA) on a sample of N = 29 publicly listed Chinese plant-based firms, this research identifies three distinct equifinal pathways to superior internationalization performance. The first is the Collaboration-Compliance configuration (Organization–Environment-driven), which is primarily characterized by the synergy between strategic partnerships and regulatory risk management. The second is the Supply Chain-Compliance-Product Diversification configuration (Technology-Environment-driven), where international success is predicated on the interplay among supply chain resilience, institutional compliance, and product variety. The third is the Full-Factor Synergy configuration (Technology-Organization-Environment jointly driven), which emphasizes a holistic coupling of technological innovation, organizational coordination, and external institutional adaptation. By uncovering these complex causal mechanisms, this study moves beyond traditional linear analysis to reveal how diverse capability configurations can lead to equivalent internationalization outcomes. The findings provide actionable strategic guidance for firms navigating the global plant-based market and offer theoretical insights for policy frameworks supporting sustainable dietary transitions. Full article

Global climate change and soil salinization pose challenges to licorice cultivation. Evaluating seed vigor based on the dynamic changes in radicle morphology is crucial for screening and cultivating licorice varieties that are tolerant to low temperatures and salts. Traditional manual measurement of licorice radicle characteristics suffers from issues such as high cost, long time consumption, and large errors. The YOLOv11 instance segmentation model in the field of deep learning offers advantages including a simple architecture, strong lightweight properties, and a unified detection-segmentation framework. Therefore, this study selected the YOLOv11 model to build a deep learning framework and used the continuous time-series crop growth vitality monitoring system to collect full-time-series images of 18 groups of licorice seeds germinating under different temperature and salt stress conditions. The YOLOv11-seg model was improved by adding a Spatial Strip Attention mechanism (SSA) to enhance the spatial correlation of radicle features, replacing ordinary convolutions with a Multi-scale Edge Detail Enhancement Module (MEEM) to optimize multi-scale feature extraction capabilities, and embedding a Normalized Weighted Distance (NWD) loss function to strengthen the segmentation ability for tiny targets. The YOLOv11-LicoSeg model was constructed for segmenting and extracting licorice radicle features and calculating root length. The experimental results showed that the mAP50 of the model’s detection reached 97.4%, mAP50–95 reached 81.7%, the mAP50 of the segmentation mask reached 97.0%, and mAP50–95 reached 78.2%. Compared with the unimproved YOLOv11-seg, the mAP50 of detection increased by 0.7%, mAP50–95 increased by 1.3%, the mAP50 of segmentation increased by 0.7%, and mAP50–95 increased by 0.8%. The linear regression coefficient between manual measurement and machine-vision measurement was 0.94218, and the goodness of fit R² was 0.94408. Using this model and the monitoring system, the morphological evolution of the licorice radicle contour characteristics over the germination time was obtained. The study indicated that the growth of licorice radicles was optimal under salt stress of 1200 µs/cm and 1800 µs/cm. YOLOv11-LicoSeg accurately segmented licorice radicles and calculated radicle length, with the performance to segment 100 licorice radicle images within 7 s. After deployment, it significantly reduced the labor cost and time consumption for acquiring licorice radicle phenotypes. In conclusion, YOLOv11-LicoSeg provides a rapid and accurate method for variety screening in licorice breeding and cultivation. Full article

Musalais is a traditional fermented beverage of the Uyghur people in Xinjiang, China. Its production involves boiling grape juice at high temperatures to concentrate it and enhance its sugar content, followed by natural fermentation. However, this high-temperature concentration process leads to a significant loss of bioactive and flavor compounds, adversely affecting the quality of the final product. Adding composite ingredients may help mitigate this quality decline. This study compares Musalais new product with traditional Musalais. Phenolic analysis showed that total monomeric phenols were 182.36 mg·L⁻¹ in the new product versus 14.76 mg·L⁻¹ in traditional Musalais. Headspace solid-phase microextraction/gas chromatography–mass spectrometry (HS-SPME/GC-MS) identified 72 volatile compounds in the new product (total content of 569,848.88 μg·L⁻¹) compared to 58 compounds (total content of 362,774.17 μg·L⁻¹) in traditional Musalais. Compared to traditional Musalais, the new product exhibits a 24.14% increase in volatile compound variety and a 57.09% increase in total concentration, with more pronounced floral, fruity, and vinous aromas, as well as higher sensory scores. Non-targeted metabolomics suggests that the new product may have superior phenolic and volatile profiles. Full article

Given the diversity and richness of China’s grain varieties, traditional physicochemical quality testing methods for rice, while providing accurate results, suffer from drawbacks such as time-consuming procedures, high costs, substantial reagent consumption, cumbersome sample preparation, and reliance on destructive or semi-destructive techniques. This study aims to employ near-infrared spectroscopy technology to establish rapid and non-destructive predictive models for key quality indicators of japonica rice. The research analyzed 133 samples from 71 widely cultivated japonica rice varieties across five major production regions in China, utilizing spectral data within a wavelength range of 660–1080 nm. Predictive models for moisture, protein, amylose, and fatty acid values were constructed using three algorithms—partial least squares regression (PLSR), ridge regression (RR), and extremely randomized trees (ERT)—linear regression and the extreme randomization tree (ERT)—their optimal parameters were determined using a 10-fold cross-validation optimization method. Eighty percent of the total dataset served as the training set, while the remaining 20% formed the test set, yielding a final test set comprising 26 samples. Performance comparisons revealed that the PLSR and RR models demonstrated superior predictive performance: the coefficient of determination (R_p²) exceeded 0.9 for all four indicators, with the R² value for fatty acid prediction reaching as high as 0.99; the root mean square error (RMSEP) of the PLSR and RR models ranged between 0.0534% and 0.3360%, confirming their high predictive accuracy. Although all ERT models (except the protein model) achieved R_p² values exceeding 0.9, their overall performance was slightly inferior to the first two methods. The protein ERT model demonstrated relatively low performance, with an R_p² value of 0.6984 on the test set, which may be attributed to the limited sample size and weak protein spectral response signals. Although the samples covered five major production regions and 71 japonica rice varieties, their distribution was uneven (multiple varieties were represented by only one or a few samples). This study provides an efficient rapid quality assessment method for japonica rice; however, the generalization ability of the models requires further validation in future studies employing larger and more balanced sample sizes. Full article

While traditional flours, such as roller-milled refined flour (RRF) and stone-milled whole-wheat flour (SWF), are subject to a trade-off between nutritional value and sensory quality, stone-milled refined flour (SRF) offers a balanced composition. Nevertheless, the functionality and baking performance of dough depend on complex macromolecular interactions beyond composition alone. Using three hard red spring (HRS) wheat varieties, this study compares the protein and starch functionality, dough rheology, and bread quality associated with SRF compared to RRF, SWF, and roller-milled whole-wheat flour (RWF). Stronger gluten formation in SRF compared to RRF was evident from its higher maximum torque (62.83 GPU) and gluten aggregation energy (1681.7 GPU) in the Glutopeak analysis. Using a Rapid Visco Analyser (RVA), lower peak viscosity (1725.25 RVU) and higher pasting temperature (89.4 °C) were observed for SRF. It also exhibited higher water absorption (68.93%) than RRF (65.98%), although their dough stability and mixing tolerance were similar. While RRF produced the highest specific bread volume (6.74 cc/g) and softest crumb (2147.13 mN), SRF achieved an intermediate volume (5.51 cc/g) with a 26.4% improvement over SWF. The correlation analysis results indicated that specific volume is positively associated with gluten aggregation parameters and negatively correlated with crumb firmness, confirming that bread quality is primarily governed by gluten structure. Overall, the use of SRF resulted in balanced dough properties and bread quality, making it a viable, nutritionally enriched alternative for both artisanal and commercial baking. Full article

Wine production is one of the most important agricultural activities worldwide, and generates significant amounts of organic by-products, particularly grape pomace. Traditionally, this was seen as waste, but currently, this residue has been reanalyzed from the perspective of the principles of the bioeconomy and circular economy, demonstrating its potential as a rich source of bioactive compounds with great potential for valorization. Its heterogeneous composition accumulates a variety of polyphenols, dietary fibers, flavonoids, phenolic acids, and other secondary metabolites that confer important biological properties, including antioxidant, anti-inflammatory, and antimicrobial activities. The chemical composition of grape pomace varies substantially according to variety, winemaking method, and extraction conditions, directly impacting its potential application. Extraction methods have progressed from traditional procedures to more advanced techniques such as ultrasound, supercritical fluids, and natural solvents, enabling the selective separation of high-value compounds. This review provides a comprehensive and critical overview of grape pomace valorization, emphasising its composition, green extraction and current industrial applications. In addition, regulatory frameworks and sustainability strategies supporting the integration of grape pomace into value-added production chains are discussed. Overall, grape pomace valorization supports waste reduction and the production of new functional products that balance economic efficiency and environmental responsibility. Full article

Food security serves as a vital cornerstone for social stability. As one of the most important staple crops globally, the quality and geographical origin of rice are directly associated with consumer health. Traditional methods for classifying rice by origin and variety rely on sensory evaluation and manual inspection, which are subject to uncertainty and human error. To address this, this paper proposes a method for classifying rice by origin and variety based on terahertz time-domain spectroscopy. Terahertz technology features the advantages of non-destructive, high-sensitivity and non-contact detection, making it well-suited for food detection. This study employs terahertz time-domain spectroscopy combined with machine learning modeling methods, using 20 types of rice as the subject of investigation, with a focus on modeling and analyzing four representative samples. Refractive index and absorption coefficient were extracted through preprocessing methods including Savitzky–Golay convolution smoothing, wavelet denoising and moving average smoothing. Modeling, classification, and detection were implemented using principal component analysis, partial least squares discriminant analysis, and least-squares support vector machine. The experimental results indicate that principal component analysis (PCA) alone performs poorly in classification tasks. However, a classification model combining PCA for dimensionality reduction with a least-squares support vector machine (SVM), following Savitzky–Golay smoothing, demonstrated the best performance, achieving a prediction accuracy of 93.3%. In an extended test involving 20 samples, the model achieved an identification accuracy of 89.6%. Quantitative metrics demonstrate the feasibility of using terahertz technology combined with optimized machine learning algorithms for classifying rice by origin and variety. Full article

The Marmara region of Türkiye, situated along the North Anatolian Fault Zone (NAFZ), constitutes one of the most seismically active and densely monitored zones globally. Given the region’s high vulnerability and the catastrophic impacts of historical events—notably the 1999 İzmit and 2023 Kahramanmara¸s sequences—there is a critical need for advanced seismic hazard risk assessment (SHRA) methods that move beyond static models. This review examines the paradigm shift from traditional geophysics to big data seismology, characterized by the “Five Vs”: volume, velocity, variety, veracity, and value. Critically, we distinguish between two fundamentally different problems: Earthquake Early Warning (EEW), which operates on sub-second timescales after rupture initiation, and probabilistic earthquake forecasting, which operates on timescales of years to decades. The study discusses how cloud-native platforms such as Azure Databricks, combined with data pipelines using Apache Kafka (version 3.5.1) and Apache Spark (version 4.1.2), enable the real-time processing of petabyte-scale seismic sensor streams. Key technological tools, including Physics-Informed Neural Networks (PINNs) and deep learning models such as PhaseNet, are analyzed for their demonstrated ability to enhance EEW systems through sub-second phase picking and automated event detection. Seismic tomography is also undergoing AI-enabled transformation, yielding higher-resolution subsurface imaging. We present statistical validation metrics and uncertainty quantification methods essential for credible hazard assessment. By addressing computational bottlenecks through hybrid computing architectures and edge computing, this framework aims to improve the warning lead time for Istanbul’s critical infrastructure. This work provides a structured roadmap for bridging the gap between traditional seismic data analysis and operational predictive analytics in the Marmara region. Full article

Camel milk is widely consumed in the world’s arid and semi-arid regions because of its favorable nutritional profile and associated human health benefits. The indigenous microbiota of raw camel milk is diverse and composed of different bacterial and fungal groups. This community drives spontaneous milk fermentation, resulting in a variety of traditional products, including Gariss, Shubat, Chal, Dhanaan, Lfrik, and Suusac (or Suusa), depending on geographic region and cultural practice. This fermented milk has improved sensory, nutritional, and health profiles, as well as an extended shelf life, compared to raw milk. Fermentation alters the microbial community structure, with lactic acid bacteria (LAB) consistently becoming dominant, while yeasts and molds are also detected in some products. These patterns have been identified using both culture-dependent and culture-independent approaches, including 16S rRNA gene sequencing and whole-genome shotgun metagenomics. However, the milk’s microbial composition is highly variable and is influenced by the original composition, geographical location, fermentation and hygiene practices. The detection of opportunistic pathogens such as E. coli, Salmonella and Listeria in some traditional products raises important food safety concerns. This review presents current knowledge on fermented camel milk microbiology using a cross-regional approach, identifying key gaps in microbial safety and process standardization to support wider acceptance and potential commercialization. Full article