Search Results (18,609)

Canarii Fructus (Canarium album) is a rich source of polyphenols with significant potential for functional food applications. This review summarizes recent advances in the composition, extraction technologies, biological activities, and utilization prospects of Canarii Fructus polyphenols (CFPs). More than 30 polyphenolic compounds have been identified, with gallic acid and ellagic acid as the major constituents, accounting for approximately 38.8% and 14.3% of the total phenolics, respectively. The fruit contains about 300 mg/100 g fresh weight of phenolic compounds. Emerging extraction technologies, including ultrasound-assisted extraction, microwave-assisted extraction, and ultrasound–microwave-assisted extraction (UMAE), have improved extraction efficiency, with UMAE achieving yields of up to 6.33% within 4.4 min. Unlike previous studies focusing primarily on phytochemical characterization or pharmacological activities, this review provides a comprehensive food-oriented perspective by integrating chemical diversity, extraction strategies, molecular mechanisms, bioavailability challenges, and functional food applications of CFPs. CFPs exhibit antioxidant, anti-inflammatory, antiviral, antibacterial, antitumor, anti-aging, hepatoprotective, and metabolic regulatory activities through pathways including Nrf2/ARE, NF-κB, PI3K/Akt, and AMPK. Representative bioactivities include α-glucosidase inhibition (IC₅₀ = 9.914 × 10⁻³ μg/mL) and regulation of lipid metabolism via AMPK activation. Particular attention is given to emerging approaches, including green extraction technologies, nanodelivery systems, and AI-assisted target discovery. Current limitations related to low bioavailability, unclear structure–activity relationships, and insufficient in vivo evidence are also discussed. Overall, CFPs represent a promising natural resource for the development of functional foods and nutraceuticals. Full article

Background/Objectives: Resveratrol, a natural polyphenol with anti-inflammatory and antioxidant properties, has emerged as a promising adjunctive agent in periodontal therapy. This systematic review and meta-analysis evaluated evidence from in vitro, in vivo, and human clinical studies regarding the effects of resveratrol on periodontal disease, with the clinical component focusing on systemically healthy non-smoking patients. Methods: Electronic searches of MEDLINE/PubMed, Scopus, Embase, and Web of Science were conducted for studies published between 2000 and 2025. Eligible studies included periodontal-related in vitro cell models, in vivo experimental periodontitis models, and randomized clinical trials assessing resveratrol as an adjunctive therapy. Data extraction, risk-of-bias assessment, and meta-analyses were conducted in accordance with PRISMA guidelines. Results: Fifteen studies were included (five in vitro, six in vivo, and five human randomized controlled trials). Meta-analysis showed the inhibitory effect of resveratrol on the lipopolysaccharide-induced protein expression of IL-1β, IL-6, TNF-α, and IL-8 in vitro, with low to moderate heterogeneity. In animal studies, resveratrol significantly attenuated ligature-induced dental alveolar bone loss, IL-1β protein, and relative mRNA expression. However, reductions in relative mRNA expressions of TNF-α and IL-6 were inconsistent and highly heterogeneous; in contrast, nanoparticle- and liposomal-modified resveratrol consistently and significantly reduced these mRNA levels. In human trials, adjunctive resveratrol was associated with improvements in probing pocket depth and clinical attachment level compared with root planing alone in patients with periodontitis, as well as reductions in bleeding and plaque indices in patients with periodontal diseases, including gingivitis and periodontitis. Conclusions: Resveratrol suppresses pro-inflammatory cytokine expression in vitro and attenuates alveolar bone loss in vivo, with enhanced and more consistent molecular effects observed using modified formulations. Preliminary clinical evidence suggests that resveratrol may be associated with modest adjunctive effects on periodontal outcomes in systemically healthy non-smokers. However, given the limited number of clinical trials, small sample sizes, heterogeneity among studies, short follow-up periods, and limited certainty of the evidence, these findings should be interpreted cautiously. Further well-designed RCTs with longer follow-up periods are required to determine their clinical relevance. Full article

Monitoring the dynamic response of structures subjected to operational loads is a key component of structural health assessment, providing valuable information for safety evaluation and maintenance planning. In the last decade, video-based measurements have received growing attention for modal identification and damage detection applications, offering a promising alternative to traditional sensor-based approaches. Unlike conventional monitoring systems, which provide discrete measurements and often require extensive instrumentation, computer vision techniques enable dense, non-contact measurements while reducing installation costs and accessibility constraints. Moreover, Motion Magnification algorithms can be combined with computer vision-based identification techniques to amplify displacements within selected frequency ranges, facilitating the detection of low-amplitude structural vibrations. In this work, a semi-automated methodology for structural identification is presented and validated through two experimental applications involving vibrating systems monitored with commercial cameras. The proposed framework combines computer vision algorithms, Motion Magnification (MM), correlation analysis, and Principal Component Analysis (PCA), the latter being adopted as a noise-reduction and dimensionality-reduction tool to extract the most informative features from large sets of time-histories. In contrast to previous studies primarily focused on damage detection and frequency evolution tracking, the present work specifically investigates the influence of key user-defined parameters on the reliability of the identified frequencies and provides practical calibration guidelines for future applications. The methodology was validated against reference measurements obtained from an optical monitoring system and it successfully identified the natural frequencies of the analysed structures with errors ranging from 0.84% to 1.75%. Sensitivity analyses performed on the region of interest size and position, as well as on the correlation threshold, demonstrated the robustness of the proposed workflow. The results confirm that the proposed approach represents a reliable, low-cost, and minimally invasive alternative to conventional dynamic monitoring techniques, while providing practical recommendations for its implementation in real-world structural health monitoring applications. Full article

Fixed-bottom or floating offshore structures are the foundations, platforms, and associated infrastructure that allow for oil and gas production systems, offshore wind turbines, and cabling. The remote nature of these structures and the harsh environment with high variability in wind, waves, currents, and weather make construction activity very difficult and unpredictable; the cost of variation in the schedule can lead to high construction vessel and personnel costs. The adoption of artificial intelligence using trends observed in historical data can help achieve more accurate construction costs and schedule predictions, reducing the capital expenditure cost of installation. A resource-significant activity, sometimes called a resource-critical activity or high-resource-demand activity, is an activity on a construction or project schedule that consumes a disproportionately large share of one or more resources compared with others. Plant Design Modelling (PDM) is a digital process that creates and manages a detailed 3D model of a building’s physical and functional characteristics and semantic information, such as cost and schedule. PDM serves as a single source of truth for multidisciplinary activities and, therefore, serves as a rich data source for various construction applications, including project scheduling and cost estimation. Neural networks (NNs), a subset of machine learning algorithms inspired by the human brain, excel at identifying patterns in complex datasets and making predictions, such as forecasting costs based on non-linear relationships and historical trends. Data from an offshore structure modification project were extracted from Aveva’s Everything PDM, focusing on installation activities to create a dataset for machine learning model training. The structured data extracted exhibit non-linear patterns; therefore, linear, regularised linear, robust linear, and the ensemble (tree-based) models and supervised neural network models with varied architecture and hyperparameter values were evaluated and compared. The best performance was obtained using the deep-optimised ANN model. The result obtained is consistent with previous studies. The neural network models show a superior ability to predict the non-linear nature of offshore construction activities’ time. Full article

Methane (CH₄) is the second most significant greenhouse gas after carbon dioxide (CO₂). Due to its high short-term global warming potential and the feasibility of emission abatement, monitoring methane point-source emissions has become a critical strategy for mitigating climate change. To address existing technical bottlenecks associated with spatial coverage limitations and surface-induced signal interference in satellite-based monitoring, this study proposes an integrated methane monitoring framework termed Multi-Band Multi-Constraint (MBMC) using Sentinel-2 MSI imagery. The MBMC framework combines a Low-Reflectance Artifact Detection (LRAD) algorithm, a Multi-Band Multi-Pass (MBMP) differential absorption retrieval model, and Integrated Mass Enhancement (IME)-based emission quantification. The LRAD module effectively suppresses artifacts caused by low-reflectance surfaces and heterogeneous backgrounds, thereby improving the signal-to-noise ratio (SNR) and retrieval accuracy of methane column enhancements. In addition, a semi-automatic plume segmentation workflow integrating morphological operations with a spatial database is developed to improve methane plume extraction and source localization. The framework was validated using data from single-blind controlled methane release experiments conducted in Arizona, USA. Results show that the proposed method achieved a mean absolute percentage error (MAPE) of 21.6% for emission rates ranging from 0.5 to 3.0 t/h, demonstrating promising performance for Sentinel-2-based screening and quantification of methane point sources, particularly for emissions above approximately 0.5 t/h under favorable observation conditions. The framework was further applied to Sentinel-2 observations over a natural gas field in Northwest China, where multiple methane point sources associated with gas gathering stations were successfully identified and quantified. The proposed framework provides a practical approach for high-resolution and high-frequency satellite monitoring of methane point sources and supports the refinement of methane emission inventories and mitigation strategies in the oil and gas sector. Full article

This review critically analyzes bioactive compounds derived from agro-industrial by-products, including polyphenols, natural pigments, dietary fiber, prebiotics, lipids, proteins, and bioactive peptides. The review examines their chemical characteristics, major agro-industrial sources, and recovery strategies, highlighting both conventional technologies and emerging green technologies, such as ultrasound-assisted extraction, supercritical fluids, and natural deep eutectic solvents (NADESs). Across compound classes, common patterns are identified, including the importance of external plant tissues as primary biological reservoirs, as well as a methodological convergence in extraction processes despite the wide chemical diversity of the molecules. Shared challenges related to compound stability, scalability, and process efficiency are also discussed. The results demonstrate that agro-industrial by-products should be understood as complex, integrated matrices rather than isolated sources of individual compounds, thereby supporting the development of unified biorefinery schemes. Unlike previous reviews focused on individual compound classes, this review integrates multiple classes of bioactive compounds, green extraction technologies, analytical characterization strategies, and industrial valorization approaches within a circular biorefinery framework. In conclusion, this review helps bridge the current fragmented understanding of waste valorization and highlights key opportunities for the sustainable development of high-value-added functional ingredients within the framework of the circular economy. Full article

Natural bioactive compounds with antioxidant properties and associated biological activities are increasingly valued for food and nutraceutical applications. This study investigated the organ-specific phenolic composition, antioxidant capacity, and enzyme inhibitory activities of Helichrysum noeanum to evaluate its potential as a multifunctional source of such compounds. Methanolic extracts from flowers, leaves, stems, and roots were analyzed for total phenolics, flavonoids, and individual compounds using LC–ESI–MS/MS, alongside antioxidant and enzyme inhibitory activities. Leaves showed the highest phenolic content (87.23 mg GAEs/g extract) and strongest antioxidant capacity (RACI = 1.19), dominated by chlorogenic acid. Roots showed the highest concentrations of caffeic and ferulic acids, while stems contained benzoic acid derivatives and quercetin. Leaf extracts exhibited the most potent reducing and radical-scavenging activities, whereas roots and stems were superior in cholinesterase and α-amylase inhibition. Correlation analysis suggested that antioxidant responses were more closely associated with total phenolic content than with total flavonoid content, whereas enzyme inhibitory activities exhibited organ-dependent variation. Overall, H. noeanum represents an organ-specific source of bioactive compounds with potential relevance for nutraceutical and food-related applications. Full article

The dust extraction impeller is a core component of the vehicle engine auxiliary system that filters dust from the intake air to ensure stable engine operation; its reliability directly affects the performance and operational safety of the vehicle. Critically, the dust extraction impeller can exhibit severe erosion wear in extreme environments, but conventional degradation testing methods are costly and require considerable time to complete. Therefore, this study conducted accelerated degradation testing using the change in impeller blade thickness as the degradation indicator and the dust concentration and impeller rotational speed as dual elevated stress factors to obtain time-series degradation data from 48 blade samples. Linear, exponential, power-law, natural logarithmic, and Gompertz models were subsequently fit to the data for a single sample, and then the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was employed to select the optimal degradation trajectory model. The accuracy of the selected linear model was verified using the data from all samples, confirming that it can be applied to predict the degradation of the dust extraction impeller over time. The contribution of this study comprises the establishment of a degradation assessment framework combining accelerated degradation testing with TOPSIS-based model selection to provide a practical basis for the reliability design and maintenance planning of vehicle dust extraction impellers operating in extreme environments. Full article

Foods rich in polyphenols are known to promote oral health by modifying the enamel pellicle. In doing so, they reduce bacterial adhesion, biofilm maturation, and erosion. The goal of this study was to screen local herbal drugs available in Central Europe for their potential suitability as part of a diet promoting oral health by targeting the initial stages of biofilm formation. To achieve this, an in situ study was conducted to evaluate the effects of the four polyphenol-rich herbal extracts of blackcurrant leaves, oak bark, horse chestnut leaves, and sweet chestnut leaves on early bacterial adhesion and biofilm formation on tooth enamel over an 8 h period. This research aimed to identify natural remedies that could support oral hygiene by targeting the initial stages of biofilm formation. Study Design and Experimental Procedures: Aqueous extracts were prepared by ultrasonic extraction. Eight human subjects wore bovine enamel slabs intraorally for 8 h. After 1 min of pellicle formation, the subjects rinsed with 8 mL of the extracts for 10 min, followed by intraoral exposure without food. An 8 h-exposure without rinse served as the negative control; 0.2% chlorhexidine gluconate (CHX) served as the positive control. After 8 h, bacterial adhesion and biofilm matrix formation on the enamel slabs were quantified ex vivo using DAPI/Concanavalin A staining and fluorescence microscopy. The LIVE/DEAD™ BacLight™ assay was used to assess bacterial viability. Statistical analysis was performed by the Mann–Whitney U test and Kruskal–Wallis test (p < 0.05), as well as the Bonferroni–Holm correction (p < 0.01). Results and Conclusions: The screened herbal drugs did not demonstrate a statistically significant impact on the number of adherent bacteria, suggesting that their mode of action may not directly interfere with bacterial adhesion mechanisms. However, all four extracts exhibited consistent trends toward reduced glucan formation and decreased bacterial viability. The observed inhibition of glucan formation indicates that these drugs may potentially target the enzymatic pathways responsible for polysaccharide synthesis. By disrupting glucan production, the structural integrity of the biofilm matrix might be compromised, which indirectly affects bacterial survival within the biofilm environment. Full article

Chlorophyll, a natural photosynthetic pigment, is gaining interest for its sustainable and eco-friendly applications in renewable energy, particularly as a photosensitizer in dye-sensitized solar cells (DSSCs). This study investigates the feasibility of chlorophyll extracted from Moringa oleifera as a natural photosensitizer in DSSCs, building on our previous work demonstrating its high chlorophyll content and long-term stability. Chlorophyll was extracted using acetone under optimal conditions (45 °C, 60 min) and applied in DSSCs comprising a TiO₂ photoanode, iodide/triiodide electrolyte, and platinum counter electrode. The TiO₂ photoanode was characterised using UV-Vis spectroscopy, FE-SEM, XRD, and Raman spectroscopy, confirming the presence of pure anatase phase TiO₂ with uniform spherical nanoparticle morphology. The fabricated DSSCs achieved a short-circuit current density of 0.197 mA cm⁻², an open-circuit voltage of 0.44 V, a fill factor of 32%, and a photoconversion efficiency (PCE) of 0.027%. While this performance is lower than the highest reported chlorophyll-based DSSC efficiency (4.6%), the results demonstrate that M. oleifera is a viable and sustainable source of chlorophyll for DSSC applications. The findings highlight the importance of dye–semiconductor interactions and suggest that further optimisation through co-sensitization, TiO₂ surface modification, and improved dye anchoring could enhance device performance. Full article

Microbiological contamination of surface water represents a critical public health concern, while conventional disinfectants face limitations such as the generation of toxic by-products and the emergence of microbial resistance. In this study, the application of an ethanolic peel extract of Citrus paradisi (grapefruit), obtained by sonication at 40 kHz for 90 min at 55 °C using a 1:4 (w/v) solvent-to-solid ratio, was evaluated as a natural alternative for bacterial reduction in contaminated waters. The Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) of the extract were first determined against Staphylococcus aureus and Escherichia coli. The extract was then applied to samples of slightly contaminated surface water with bacterial loads between 10³–10⁴ CFU/mL and turbidity of 150 NTU, as well as to highly contaminated surface water with bacterial loads ≥10⁵ CFU/mL and turbidity of 250 NTU. Bacterial removal was assessed at 6, 12, and 24 h. FTIR and UV-Vis characterization of the extract confirmed the presence of flavonoids (naringin), terpenes (limonene), and phenolic compounds. Results showed MIC/MBC values of 2.5/5.0 mg/mL for S. aureus and 5.0/10.0 mg/mL for E. coli. In slightly contaminated water, the extract at 5.0 mg/mL achieved complete (100%) removal of both microorganisms after 12 h, whereas in highly contaminated water, removals ranged from 80–90% for Staphylococcus spp. and E. coli. Statistical analysis (ANOVA, Bonferroni) demonstrated significant differences (p < 0.05) between the extract and ethanol. These findings indicate that Citrus paradisi extract constitutes an effective, sustainable, and low-cost natural alternative for bacterial reduction in surface waters, contributing to the valorization of agro-industrial residues and to the achievement of Sustainable Development Goal (SDG) 6. Full article

Date syrup is a product processed from date (Phoenix dactylifera L.) flesh through a multi-unit operation process. This nutritious liquid is rich in carbohydrates, dietary fibres, polyphenols, minerals, and vitamins, and may provide functional and bioactive compounds with potential health-promoting properties. In this review, a summary of current literature related to the technological aspects of date syrup production was given with a focus on traditional, conventional, and novel extraction methods. Further, a systematic analysis of available data describing the compositional quality of date syrup based on biochemical, functional, antioxidant, and pro-healthy properties was highlighted. Finally, the potential applications of date-based syrup in several fields of food industry were discussed by highlighting the importance of using date syrup as an emerging alternative for sugar substitution in numerous food products and formulations. Full article

The aim of this study was to formulate and optimize a novel functional ingredient based on thermally treated skimmed goat milk enriched with Agrocybe aegerita mushroom extract (ME) and grape pomace seed extract (GPE), intended for application in a dehydrated soup model. A central composite design was applied for preliminary optimization and the formulation based on antioxidant properties. The optimized ingredient exhibited enhanced antioxidant activity, with GPE identified as the dominant factor influencing the responses. However, deviations between predicted and experimental values were observed, reflecting moderate model fitting and differences in assay mechanisms. ATR-FTIR spectra were dominated by milk compounds, while DLS and electrophoretic analysis revealed structural modifications, including polymodal particle size distribution and alterations in the protein profile, indicating interactions between milk proteins, polyphenols, and mushroom-derived compounds. UHPLC-QToF-MS analysis confirmed a high content of grape-derived phenolic compounds. Following simulated gastrointestinal digestion, several phenolic compounds were detected in the soluble fraction, with catechin and ethyl gallate exhibiting the highest bioaccessibility (12.58% and 4.54%). The enriched ingredient showed modified techno-functional properties, including reduced emulsifying capacity but improved foaming behavior, which was attributed to protein structural changes and intermolecular interactions. Application in a dehydrated soup model demonstrated good solubility, stability, and high sensory acceptability without negative effects on flavor. Furthermore, the enriched soup showed enhanced antioxidant properties after simulated gastrointestinal digestion. The developed formulation represents a promising natural functional ingredient, combining enhanced bioactive properties with satisfactory technological performance. Full article

This study aims to build a spatial model for selecting the optimal site for a radio astronomy observatory in southern Jordan. Geographic Information Systems (GISs) and Multi-Criteria Decision Analysis (MCDA)-based methodology were used in this study to develop a spatial model for choosing the best location for a radio astronomy observatory in southern Jordan. The criteria were weighted using a hybrid framework that combined the Analytic Hierarchy Process (AHP) and the entropy method to account for the actual spatial diversity of the data, in addition to expert judgment. The study assesses site suitability by considering several environmental and logistical factors that mitigate radio frequency interference (RFI), including elevation, cloud cover, artificial light pollution, and accessibility. A final map highlighting the optimal areas for radio astronomy observatories in southern Jordan has been created. The study methodology started with MCDA, and was followed by several stages, including visual evaluation, overlay analysis, establishment of 500 m buffer zones, extraction of the “Very High Suitability” class, and conversion to a transparent vector layer that is free from urban overlap and electromagnetic interference. The results show that the majority of large observatories (10 km²; equivalent to ≥10,000,000 m²) are located in Aqaba and Ma’an, which offer natural isolation and wide expanses ideal for global projects. Medium observatories (0.5–10 km²; equivalent to 500,000–10,000,000 m²) were generally identified at a reasonable cost in Ma’an and Aqaba, with the possibility of radio surveillance and infrastructure expansion. Many small observatories (0.01–0.5 km²; equivalent to 10,000–500,000 m²) were constructed near academic institutions, providing viable, easily accessible places for university research with little regulatory restraints. This research contributes to national astronomy infrastructure planning and serves as a model for other countries experiencing dry or semi-arid climates. It also offers decision-makers a useful spatial database. Full article

Effective condition monitoring (CM) in the mining industry is crucial for operational excellence, given the harsh environments, continuous operation, and high-value nature of assets. Traditional fault diagnosis methods like vibration analysis often prove inadequate due to signal noise, logistical challenges for sensor placement, and limitations in detecting subtle failures. This paper addresses these challenges by proposing an advanced contactless diagnostic system that integrates Infrared Thermography (IRT) with an optimized Convolutional Neural Network (CNN) for detecting machinery faults in mining operations. The core of the approach involves a customized ResNet-50 architecture, chosen for its inherent ability to extract hierarchical features directly from raw thermal image data, thereby circumventing the laborious and error-prone process of manual feature engineering. Recognizing the profound impact of hyperparameters on model performance, a novel optimization strategy is developed. This strategy utilizes an amended Salp Swarm Algorithm (SSA), which incorporates a Levy flight mutation strategy and improved position update mechanisms to enhance its exploration capabilities and prevent premature convergence, ensuring a thorough search of the complex hyperparameter space. The proposed methodology is rigorously evaluated using thermal images acquired from a heavy-duty belt conveyor system at the JARO S.A. mine. The optimized ResNet-50 model achieved a remarkable validation accuracy of 97.22%, demonstrating superior performance. Comparative analysis showed that our model significantly outperformed other state-of-the-art deep learning architectures, such as InceptionV3 and ResNet-18, as well as other metaheuristic optimization algorithms, yielding a 15.6% improvement over the basic SSA. This robust performance, combined with efficient convergence, underscores the model’s capacity for accurate and timely fault identification, paving the way for proactive maintenance, reduced downtime, and enhanced safety in demanding mining environments. Full article