Search Results (648)

Cannabis sativa L. is a versatile plant with applications in various sectors such as agriculture, medicine, food, and cosmetics. The therapeutic properties of cannabis are often linked to its secondary compounds. The worldwide cannabis market is undergoing swift changes due to varying legal frameworks. Medicinal cannabis (as a heterozygous and dioecious species) is distinct from most annual crops grown in controlled environments, typically propagated through stem cutting rather than seeds to ensure genetic uniformity. Consequently, as with any commercially cultivated crop, biomass yield plays a crucial role in overall productivity. The key factors involved in cultivation conditions, such as successful root establishment, stress tolerance, and the production cycle duration, are critical for safeguarding, improving, and optimizing plant yield. Grafting is a long-established horticultural practice that mechanically joins the scion and rootstock of distinct genetic origins by merging their vascular systems. This approach can mitigate undesirable traits by leveraging the strengths of particular plants, proving beneficial to various applications. Grafting is not used commercially in Cannabis. Only three very recent investigations suggest that grafting holds significant promise for enhancing both the agronomic and medicinal potential of Cannabis. This review critically examines the latest advancements in cannabis grafting and explores prospects for improving biomass (stem, root, flower, etc.) yield and secondary metabolite production. Full article

As eco-friendly processes become central to modern organic synthesis, plant-based materials are emerging as attractive alternatives for both nanoparticle fabrication and catalysis. In this study, we explore the use of clove extract, a natural and renewable resource, for the green synthesis of copper oxide (CuO) nanoparticles and their subsequent application in organic transformations. Clove extract was employed to reduce copper chloride via a simple co-precipitation method under mild conditions, yielding CuO nanoparticles characterized by XRD, FTIR, and SEM-EDX techniques. These nanoparticles were then used as catalysts in the copper-catalyzed azide–alkyne cycloaddition (CuAAC) to afford eugenol-based 1,2,3-triazoles in excellent yields. This dual use of clove extract exemplifies a sustainable approach that merges natural product valorization with efficient catalysis for triazole synthesis. Full article

Oenological residues may cause environmental pollution when processing does not significantly reduce volume and/or harmful conditions. The lack of proper valorisation alternatives entails high disposal costs and resource inefficiency that jeopardise the sustainability and competitiveness of the industry. Interestingly, wine by-products are underappreciated sources of multipurpose bioactive compounds, such as anthocyanins, associated with health benefits. Alternatively, transforming oenological by-products into valuable co-products will promote sustainability and thus, create new business opportunities. In this context, the present study has assessed the applicability of winery by-products (grape pomace and wine lees) as ingredients to develop new functional kombucha-analogous beverages “3S” (safe, salubrious, and sustainable) by the Symbiotic Culture of Bacteria and Yeast (SCOBY). Concerning the main results, during the kombucha’s development, the fermentation reactions modified the physicochemical parameters of the beverages, namely pH, total soluble solids, acetic acid, ethanol, and sugars, which remained stable throughout the monitored shelf-life period considered (21 days). The fermented beverages obtained exhibited high anthocyanin concentration, especially when using wine lees as an ingredient (up to 5.60 mg/L at the end of the aerobic fermentation period (10 days)) compared with the alternative beverages produced using grape pomace (1.69 mg/L). These findings demonstrated that using winery by-products for the development of new “3S” fermented beverages would provide a dietary source of bioactive compounds (mainly anthocyanins), further supporting new valorisation chances and thus contributing to the competitiveness and sustainability of the winery industries. This study opens a new avenue for cross-industry innovation, merging fermentation traditions with a new eco-friendly production of functional beverages that contribute to transforming oenological residues into valuable co-products. Full article

With the growing impact of environmental challenges, the need for well-planned and effectively executed actions to support progress and sustainable social development has become increasingly evident. Geoparks play a vital role in this endeavor by fostering the development of products that celebrate local heritage and promote its conservation, utilizing the natural and cultural resources unique to each region in sustainable ways. Geoproducts, in particular, aim to enrich cultural identity and elevate the value of the landscape and geodiversity by integrating communities into innovative approaches and technologies, engaging them in commercialization, and ensuring sustainability alongside social inclusion. Within the framework of the Safi Geopark Project, this article delves into the concept of geoproducts, their definitions, and their potential to bolster local identity and social and economic development. Leveraging the abundant geological and cultural resources of Safi province, the study presents both tangible and intangible geoproducts that merge traditional craftsmanship with modern sustainability practices. Notable examples include ammonite-inspired ceramics, educational materials, and eco-friendly cosmetics, each carefully designed to reflect and celebrate the region’s geoheritage. This article underscores the crucial role of community involvement in the creation of geoproducts, highlighting their impact on conservation, education, and the promotion of sustainable tourism. By proposing actionable strategies, this study not only broadens the understanding of geoproducts within geoparks but also reinforces their importance as instruments for regional development, heritage conservation, and sustainable economic growth. Full article

Plant diseases significantly undermine agricultural productivity. This study introduces an improved YOLOv10n model named WD-YOLO (Weighted and Double-scale YOLO), an advanced architecture for efficient plant disease detection. The PlantDoc dataset was initially enhanced using data augmentation techniques. Subsequently, we developed the DSConv module—a novel convolutional structure employing double-scale weighted convolutions that dynamically adjust to different scale perceptions and optimize attention allocation. This module replaces the conventional Conv module in YOLOv10. Furthermore, the WTConcat module was introduced, dynamically merging weighted concatenation with a channel attention mechanism to replace the Concat module in YOLOv10. The training of WD-YOLO incorporated knowledge distillation techniques using YOLOv10l as a teacher model to refine and compress the architectural learning. Empirical results reveal that WD-YOLO achieved an mAP50 of 65.4%, outperforming YOLOv10n by 9.1% without data augmentation and YOLOv10l by 2.3%, despite having significantly fewer parameters (9.3 times less than YOLOv10l), demonstrating substantial gains in detection efficiency and model compactness. Full article

We present the validation of the latest version F Geophysical Data Records (GDR-F) for the TOPEX/POSEIDON (T/P) altimetry satellite mission. The GDR-F products represent a major evolution with respect to the preceding version B Merged Geophysical Data Records (MGDR-B) that were released more than two decades ago. Specifically, the numerical retracking of the altimeter waveforms significantly mitigates long-standing issues in the TOPEX altimeter measurements, such as drifts and hemispherical biases in the altimeter range and significant wave height. Additionally, GDR-F incorporates updated geophysical model standards consistent with current altimeter missions, improved sea state bias corrections, end-of-mission calibration for the microwave radiometer, and refined orbit ephemeris solutions. These enhancements notably decrease the variance of the Sea Surface Height Anomaly (SSHA) measurements, with along-track SSHA variance reduced by 26 cm² compared to MGDR-B and crossover SSHA variance lowered by 1 cm². GDR-F products also demonstrate improved consistency with Jason-1 measurements during their tandem mission phase, reducing the standard deviation of differences from 6 cm to 4 cm when compared to Jason-1 GDR-E data. These results confirm that GDR-F products offer a more accurate and consistent T/P data record, enhancing the quality of long-term sea level studies and supporting inter-mission altimetry continuity. Full article

Tainan’s Cigu, located on Taiwan’s southwestern coast, is a prominent aquaculture hub known for its extensive ponds, tidal flats, and lagoons. This study explored the novel integration of kayaking within aquavoltaic (APV) aquaculture ponds, creating a unique hybrid tourism landscape that merges industrial land use (aquaculture and energy production) with nature-based recreation. We investigated the relationships among facility maintenance and safety professionalism (FM), the perceived value of kayaking training (PV), and green energy and sustainable development recognition (GS) within these APV systems in Cigu, Taiwan. While integrating recreation with renewable energy and aquaculture is an emerging approach to multifunctional land use, the mechanisms influencing visitors’ sustainability perceptions remain underexplored. Using data from 613 kayaking participants and structural equation modeling, we tested a theoretical framework encompassing direct, mediated, and moderated relationships. Our findings reveal that FM significantly influences both PV (β = 0.68, p < 0.001) and GS (β = 0.29, p < 0.001). Furthermore, PV strongly affects GS (β = 0.56, p < 0.001). Importantly, PV partially mediates the relationship between FM and GS, with the indirect effect (0.38) accounting for 57% of the total effect. We also identified significant moderating effects of APV coverage, guide expertise, and operational visibility. Complementary observational data obtained with underwater cameras confirm that non-motorized kayaking causes minimal ecological disturbance to cultured species, exhibiting significantly lower behavioral impacts than motorized alternatives. These findings advance the theoretical understanding of experiential learning in novel technological landscapes and provide evidence-based guidelines for optimizing recreational integration within production environments. Full article

Coastal and maritime regions and their entities face accelerated degradation due to the combined effects of environmental stressors and anthropogenic activities. Coastal degradation can be identified, visualized and estimated through periodic monitoring over a region of interest using earth observation, climate, meteorological, seasonal, waves, sea level rising, and other ocean- and maritime-related datasets. Usually, these datasets are provided through different sources, in different structures or data types; in many cases, a complete dataset can be large in size and needs some kind of preprocessing (information filtering) before use in the intended application. Recently, the term data cube introduced in the scientific community and frameworks like Google Earth Engine and Open Data Cubes have emerged as a solution to earth observation data harmonization, federation, and exchange framework; however, these sources either completely lack the ability to process climate, meteorological, waves, sea lever rising, etc., data from open sources, like CORDEX and WCRP, or preprocessing is required. This study describes and utilizes the Ocean-DC framework for modular earth observation and other data types to resolve major big data challenges. Compared to the already existing approaches, the Ocean-DC framework harmonizes several types of data and generates ready-to-use data cubes products, which can be merged together to produce high-dimensionality visualization products. To prove the efficiency of the Ocean-DC framework, a case study at Crete Island, emphasizing the Port of Heraklion, demonstrates the practical utility by revealing degradation trends via time-series analysis of several related remote sensing indices calculated using the Ocean-DC framework. The results show a significant reduction in processing time (up to 89%) compared to traditional remote sensing approaches and optimized data storage management, proving its value as a scalable solution for environmental resilience, highlighting its potential use in early warning systems and decision support systems for sustainable coastal infrastructure management. Full article

Traditionally, the microcellular foaming process has aimed to generate uniform cell structures by applying heat uniformly to all surfaces of a polymer. Homogeneous cell distribution is known to enhance the mechanical properties and durability of the final product. However, the ability to engineer a gradient in cell density offers potential advantages for specific functional applications, such as improved sound absorption and thermal insulation. In this study, a controlled thermal gradient was introduced by heating only one side of a fully CO₂-saturated poly(methyl methacrylate) (PMMA) specimen. This approach allowed for the formation of a cell density gradient across the sample thickness. The entire process was conducted using a solid-state batch foaming technique, commonly referred to as the microcellular foaming process. A one-sided heating strategy successfully induced a spatial variation in cell morphology. Furthermore, a coalescence function was developed to account for cell merging behavior, enabling the construction of a predictive model for local cell density. The proposed model accurately captured the evolution of cell density gradients under asymmetric thermal conditions and was validated through experimental observations, demonstrating its potential for precise control over foam structure in saturated PMMA systems. Full article

Gridded precipitation datasets (GPDs) have complemented gauge-based measurements in global hydrology by providing spatiotemporally continuous rainfall estimates for streamflow prediction. However, these datasets suffer from uncertainties in space and time, particularly in complex terrains like the Himalayas. Merging multi-GPDs offers a potential solution to reduce such uncertainties, but the actual contribution of the merged product to hydrological modeling remains underexplored in data-scarce and topographically complex regions. Here, we applied a gauge-independent merging technique called Signal-to-Noise Ratio optimization (SNR-opt) to merge three precipitation products: ERA5, SM2RAIN, and IMERG-late. The resulting Merged Gridded Precipitation Dataset (MGPD) was evaluated using the hydrological model (HYMOD) across three major river basins in the Central Himalayas (Koshi, Narayani, and Karnali). The results show that MGPD significantly outperforms the individual GPDs in streamflow simulation. This is evidenced by higher Nash–Sutcliffe Efficiency (NSE) values, 0.87 (Narayani) and 0.86 (Karnali), compared to ERA5 (0.83, 0.82), SM2RAIN (0.83, 0.85), and IMERG-Late (0.82, 0.78). In Koshi, the merged product (NSE = 0.80) showed slightly lower performance than SM2RAIN (NSE = 0.82) and ERA5 (NSE = 0.81), likely due to the poor performance of IMERG-Late (NSE = 0.69) in this basin. These findings underscore the value of merging precipitation datasets to enhance the accuracy and reliability of hydrological modeling, especially in ungauged or data-scarce mountainous regions, offering important implications for water resource management and forecasting. Full article

Modern audio production workflows often require significant manual effort during the initial session preparation phase, including track labeling, format standardization, and gain staging. This paper presents a rule-based and Machine Learning-assisted automation system designed to minimize the time required for these tasks in Digital Audio Workstations (DAWs). The system automatically detects and labels audio tracks, identifies and eliminates redundant fake stereo channels, merges double-tracked instruments into stereo pairs, standardizes sample rate and bit rate across all tracks, and applies initial gain staging using target loudness values derived from a Genetic Algorithm (GA)-based system, which optimizes gain levels for individual track types based on engineer preferences and instrument characteristics. By replacing manual setup processes with automated decision-making methods informed by Machine Learning (ML) and rule-based heuristics, the system reduces session preparation time by up to 70% in typical multitrack audio projects. The proposed approach highlights how practical automation, combined with lightweight Neural Network (NN) models, can optimize workflow efficiency in real-world music production environments. Full article

Accurate extraction of weak fault information from non-stationary vibration signals collected by vibration sensors is challenging due to severe noise and interference. While variational mode decomposition (VMD) has been effective in fault diagnosis, its reliance on predefined parameters, such as center frequencies and mode number, limits its adaptability and performance across different signal characteristics. To address these limitations, this paper proposes an improved variational mode decomposition (IVMD) method that enhances diagnostic performance by adaptively determining key parameters based on scale space representation. In concrete, the approach constructs a scale space by computing the inner product between the signal’s Fourier spectrum and a Gaussian function, and then identifies both the mode number and initial center frequencies through peak detection, ensuring more accurate and stable decomposition. Moreover, a multipoint kurtosis (MKurt) criterion is further employed to identify fault-relevant components, which are then merged to suppress redundancy and enhance diagnostic clarity. Experimental validation on locomotive bearings with inner race faults and compound faults demonstrates that IVMD outperforms conventional VMD by effectively extracting fault features obscured by noise. The results confirm the robustness and adaptability of IVMD, making it a promising tool for fault diagnosis in complex industrial environments. Full article

The thermal resistance of carbon fiber–reinforced poly(phenylene sulfide) to harsh oxidative conditions is investigated through thermogravimetric experiments performed in an oxygen atmosphere. While these materials usually show great resistance against thermal decomposition in a nitrogen atmosphere, the experiments in oxygen reveal the total decomposition of both the matrix and the carbon fibers. The Gram–Schmidt signal, obtained by coupling thermogravimetric analysis in standard conditions with Fourier-transform infrared spectroscopy, exhibits multiple events, evidencing that the decomposition proceeds through distinct stages. The first step characterizes the char formation, while the second relates to its oxidative decomposition. A third step, only observed for composites, is interpreted as the signature of the oxidative decomposition of carbon fibers. To mimic the sudden elevation of temperature encountered during a fire, the analyses are performed at rates of up to 500 K min⁻¹. These specific experimental conditions reveal a complex dependence of the thermogravimetric signature on the heating rate. Independent of the atmosphere, nitrogen or oxygen, the characteristic temperature of decomposition follows a bell-shape trend, resulting from the combination of lag effects and thermal-conductivity limitations. Additionally, the increase of the heating rate causes the Gram–Schmidt signal to evolve toward a broad peak with indistinct events. To investigate whether these changes affect the decomposition products, the infrared spectra, continuously recorded to probe the whole decomposition, are compared with those from the database. The char formation is characterized by the production of sulfur dioxide, while carbon dioxide is the main product emitted during both char and fiber oxidative decomposition. Owing to the merging of the decomposition stages, sulfur-dioxide detection is partly supplanted by that of carbon dioxide under fast elevations of temperature. Full article

This study examines the combination of overhang constraints and Reliability-Based Topology Optimization (RBTO) in additive manufacturing (AM). AM offers intricate component production but faces challenges due to support structures. Incorporating overhang constraints in topology optimization enables self-supporting structures. RBTO addresses uncertainties in design variables to enhance reliability. This research investigates build direction parameter solutions using deterministic and RBTO algorithms. Topological properties, compliance, sensitivity, and density filters are assessed, alongside optimization techniques like Method of Moving Asymptotes (MMA) criterion and Optimality Criteria (OC). In numerical experiments on the MBB beam, the AM-RBTO algorithm reduced 3D printing time by approximately 18.3% and improved structural performance by lowering the objective function value by 1.85% compared to conventional RBTO. Results contribute to merging overhang constraints and RBTO in AM topology optimization, improving design by considering uncertainties. The study enhances computational efficiency and stability in optimizing build direction parameters, offering valuable insights for future AM applications. Full article

Interoperability between digital models in the manufacturing and AEC domains is a critical issue in the building design of complex systems. Despite the adoption of well-established standards such as STEP (STandard for the Exchange of Product data, ISO 10303-21) for the industrial domain and IFC (Industry Foundation Classes, ISO 16739-1) for the construction domain, communication between these domains is still limited due to differences in conceptual models, levels of detail, and application purposes. Existing solutions for conversion between these formats are few, often proprietary, and not always suitable to ensure full semantic integration in BIM (Building Information Modeling) flows. This study proposes a methodological framework for structured conversion from STEP to IFC-SPF (STEP Physical File), based on information and geometric simplification and data enrichment. The process includes the elimination of irrelevant components, simplification of geometries, merging assemblies, and integration of data useful to the building context. The experimental implementation, carried out using the Bonsai extension for Blender, demonstrates a substantial reduction in geometric complexity and computational load, while maintaining data consistency required for integration into BIM processes. This approach emerges as a scalable, affordable, and sustainable solution for interoperability between industrial and civil models, even in professional environments lacking advanced software development skills. Full article