Search Results (23,673)

Agricultural activities are major contributors to global greenhouse gas (GHG) emissions, with methane (CH₄) and nitrous oxide (N₂O) emissions accounting for 40% and 60% of total agricultural emissions, respectively. Therefore, developing effective emission reduction pathways in agriculture is crucial for achieving carbon budget balance. This article synthesizes the impact of farmland management practices on GHG emissions, evaluates prevalent accounting methods and their applicable scenarios, and proposes mitigation strategies based on systematic analysis. The present review (2000-2025) indicates that fertilizer management dominates research focus (accounting for over 50%), followed by water management (approximately 18%) and tillage practices (approximately 14%). Critically, the effects of these practices extend beyond GHG emissions, necessitating concurrent consideration of crop yields, soil health, and ecosystem resilience. Therefore, it is necessary to conduct joint research by integrating multiple approaches such as water-saving irrigation, conservation tillage and intercropping of leguminous crops, so as to enhance productivity and soil quality while reducing emissions. The GHG accounting framework and three primary accounting methods (In situ measurement, Satellite remote sensing, and Model simulation) each exhibit distinct advantages and limitations, requiring scenario-specific selection. Further refinement of these methodologies is imperative to optimize agricultural practices and achieve meaningful GHG reductions. Full article

To meet stringent efficiency and environmental targets, future gas turbines require increased turbine inlet temperatures while maintaining low NO${}_x$ emissions and accommodating hydrogen-blended fuels. Axially staged combustion has emerged as a key technology to address these challenges. This paper presents a mathematical model for the rapid prediction of NO emissions in axially staged combustors fueled with hydrogen-blended methane. The model integrates a simplified thermal NO mechanism with a set of dimensionless staging variables, providing a unified description of flow, mixing, and reaction processes. Its accuracy was validated against a detailed chemical reaction network (CRN). The model was applied to identify feasible low-emission staging windows across different hydrogen-blending ratios and to systematically analyze the effects of secondary-stage mixing quality, operating parameters, and fuel composition on optimal staging and emissions. Results demonstrate that coordinating the combustion strategies of the primary and secondary stages enables effective NO control across a wide fuel range. This work provides a theoretical foundation for the design of low-emission, fuel-flexible axially staged combustors. Full article

Periapical lesions (PALs) are a common sequela of pulpal pathology, and accurate radiographic detection is essential for successful endodontic diagnosis and treatment outcome. With recent advancements in Artificial Intelligence (AI), deep learning systems have shown remarkable potential to enhance the diagnostic accuracy of PALs. This study highlights recent evidence on the use of AI-based systems in detecting PALs across various imaging modalities. These include intraoral periapical radiographs (IOPAs), panoramic radiographs (OPGs), and cone-beam computed tomography (CBCT). A literature search was conducted for peer-reviewed studies published from January 2021 to July 2025 evaluating artificial intelligence for detecting periapical lesions on IOPA, OPGs, or CBCT. PubMed/MEDLINE and Google Scholar were searched using relevant MeSH terms, and reference lists were hand screened. Data were extracted on imaging modality, AI model type, sample size, subgroup characteristics, ground truth, and outcomes, and then qualitatively synthesized by imaging modality and clinically relevant moderators (i.e., lesion size, tooth type and anatomical surroundings, root-filling status and effect on clinician’s performance). Thirty-four studies investigating AI models for detecting periapical lesions on IOPA, OPG, and CBCT images were summarized. Reported diagnostic performance was generally high across radiographic modalities. The study results indicated that AI assistance improved clinicians’ performance and reduced interpretation time. Performance varied by clinical context: it was higher for larger lesions and lower around complex surrounding anatomy, such as posterior maxilla. Heterogeneity in datasets, reference standards, and metrics limited pooling and underscores the need for external validation and standardized reporting. Current evidence supports the use of AI as a valuable diagnostic platform adjunct for detecting periapical lesions. However, well-designed, high-quality randomized clinical trials are required to assess the potential implementation of AI in the routine practice of periapical lesion diagnosis. Full article

Background/Objectives: Effective airway management during pediatric cardiopulmonary resuscitation (CPR) is crucial but technically challenging, especially during continuous chest compressions. While direct laryngoscopy with Macintosh (MAC) or Miller (MIL) blades remains the standard, optical devices such as the VieScope (VSL) may enhance performance under dynamic resuscitation conditions. This study compared first-pass success and intubation time, as well as procedural difficulty and glottic visualization, of MAC, MIL, and VSL during simulated pediatric cardiopulmonary resuscitation. Methods: This prospective, randomized crossover simulation study involved 53 medical students. Participants performed endotracheal intubation on a high-fidelity manikin simulating a 5-year-old pediatric patient using MAC, MIL, and the Bebé VieScope laryngoscope. Each technique was evaluated in two scenarios: with and without continuous chest compressions. Results: Without chest compressions, first-pass success (FPS) and intubation time varied significantly between techniques. VSL achieved the highest FPS (100%; p = 0.032) and the shortest intubation time (27.9 ± 9.2 s; p = 0.040), performing faster than MIL and achieving higher FPS than MAC. Visualization quality, ease of intubation, and optimization maneuvers were similar across techniques. During continuous chest compressions, all outcomes differed significantly. FPS increased from MAC to MIL and VSL (p = 0.001), with MAC showing the lowest success rate. VSL showed the shortest intubation time (35.9 ± 13.0 s; p < 0.001), better glottic visualization, easier intubation, and fewer optimization maneuvers, followed by MIL. Conclusions: In this simulated pediatric cardiac arrest model, the VieScope laryngoscope demonstrated superior overall performance, especially during uninterrupted chest compressions. Optical tubular laryngoscopy may therefore provide clinically relevant benefits in pediatric resuscitation where maintaining high-quality chest compressions is crucial. Given the manikin-based design of this study, confirmation of these findings in clinical pediatric cardiac arrest settings will require further prospective clinical investigation. Full article

The development of functional biomaterials based on natural polymers has gained increasing relevance due to the growing demand for sustainable and bioactive alternatives for biomedical and technological applications. In this study, chitosan was obtained from shrimp exoskeletons and used to formulate active films enriched with Mexican propolis, aiming to evaluate the influence of the extract on the physicochemical and functional properties of the resulting biomaterial. Propolis was incorporated into the chitosan film-forming solution at a final concentration of 1.0% (v/v). The propolis employed met the requirements of the Mexican Official Standard NOM-003-SAG/GAN-2017 regarding flavonoid content, total phenolic compounds, and antimicrobial activity; additionally, it was evaluated through antioxidant activity, hemolysis, and acute toxicity (LD₅₀) assays to provide a broader biological and safety assessment. The extracted chitosan exhibited a degree of deacetylation of 74% and characteristic FTIR spectral features comparable to those of commercial chitosan, confirming the quality of the obtained polymer. Chitosan–propolis films exhibited antimicrobial activity against Staphylococcus aureus, Escherichia coli, and Candida albicans, whereas pure chitosan films showed no inhibitory effect. Thermal analyses (TGA/DSC) revealed a slight reduction in thermal stability due to the incorporation of thermolabile polyphenolic compounds, along with increased thermal complexity of the system. SEM observations demonstrated reduced microbial adhesion and marked morphological damage in microorganisms exposed to the functionalized films. Overall, the incorporation of Mexican propolis enabled the development of a hybrid biomaterial with enhanced antimicrobial performance and potential application in wound dressings and bioactive coatings. Full article

Coastal lakes require monitoring approaches that capture spatial and temporal variability beyond the limits of conventional in situ measurements. In this study, a SIGMaL framework (Satellite–In situ–GIS-multicriteria decision analysis (MCDA)–Machine Learning (ML)) was developed, a unified methodology that integrates in situ monitoring, GIS MCDA-derived water quality index (WQI), satellite imagery, and ML models for comprehensive coastal lake water quality assessment. A WQI, derived from a 12-month series of in situ measurements and environmental parameters, was used alongside four physicochemical parameters measured by a multiparameter probe. First, satellite reflectance from each sensor was used to train a set of nine regression models for modelling electrical conductivity (EC), turbidity, water temperature (WT), and dissolved oxygen (DO). Second, convolutional neural networks (CNNs) with spectral and temporal inputs were trained to classify WQI classes, enabling a cross-sensor evaluation of their suitability for lake water quality monitoring. Third, the trained CNNs were applied to generate WQI maps for a subsequent 12-month period without in situ data. Across all analyses, WQI-based models provided more stable and accurate models than those trained on raw parameters. Sentinel-2 achieved the most consistent WQI performance (AUC ≈ 1.00, R² ≈ 0.84), PlanetScope captured fine-scale spatial detail (R² ≈ 0.77), while Landsat 8–9 was most effective for WT but less reliable for multi-class WQI discrimination. Sentinel-2 is recommended as the primary satellite sensor for WQI mapping within the SIGMaL framework. These findings demonstrate the advantages of WQI-based modelling and highlight the potential of ML–remote sensing integration to support coastal lake water quality monitoring. Full article

Understanding the complex interplay between environmental factors and fruit quality development requires sophisticated analytical approaches linking cellular architecture to environmental conditions. This study introduces a novel application of dual-resolution X-ray computed tomography (CT) for the non-destructive characterization of apple internal tissue architecture in relation to fruit growth, thereby advancing beyond traditional methods that are primarily focused on postharvest analysis. By extracting detailed three-dimensional structural parameters, we reveal tissue porosity and heterogeneity influenced by crop load, maturity timing and canopy position, offering insights into internal quality attributes. Employing correlation analysis, Principal Component Analysis, Canonical Correlation Analysis, and Structural Equation Modeling, we identify temperature as the primary environmental driver, particularly during early developmental stages (45 Days After Full Bloom, DAFB), and uncover nonlinear, hierarchical effects of preharvest environmental factors such as vapor pressure deficit, relative humidity, and light on quality traits. Machine learning models (Multiple Linear Regression, Random Forest, XGBoost) achieve high predictive accuracy (R² > 0.99 for Multiple Linear Regression), with temperature as the key predictor. These baseline results represent findings from a single growing season and require validation across multiple seasons and cultivars before operational application. Temporal analysis highlights the importance of early-stage environmental conditions. Integrating structural and environmental data through innovative visualization tools, such as anatomy-based radar charts, facilitates comprehensive interpretation of complex interactions. This multidisciplinary framework enhances predictive precision and provides a baseline methodology to support precision orchard management under typical agricultural variability. Full article

Background/Objectives: Adult spinal deformity (ASD) management often requires pelvic fixation, with S2 alar–iliac (S2AI) screws emerging as an alternative to traditional iliac screws. Despite multiple meta-analyses comparing these techniques, the methodological quality of these syntheses and technical heterogeneity across primary studies significantly impact their conclusions and subsequent clinical decision-making. This systematic review evaluates the evidence quality of meta-analyses comparing S2AI with traditional iliac screws for ASD management, focusing on methodological rigor, primary study overlap, and clinical heterogeneity. Methods: PubMed, Cochrane, and Epistemonikos were searched for meta-analyses comparing S2AI with iliac screws for patients with ASD. The Quality of Reporting of Meta-analyses (QUOROM) checklist and the revised Assessment of Multiple Systematic Reviews (AMSTAR 2) tool were adopted to assess the methodological quality. Primary study overlap was evaluated using the Corrected Covered Area (CCA) method. Clinical heterogeneity was assessed by examining characteristics of studies included in ≥67% of meta-analyses. Results: From a total of 29 publications, six meta-analyses met the inclusion criteria (4807 patients; mean age: 59 years; 33% female). All included meta-analyses exhibited critically low methodological quality per AMSTAR-2, with common flaws including failure to provide lists of excluded studies and lack of a priori protocols. Very high primary study overlap was observed (CCA: 31%), with only 11% (2 of 19) primary studies included in all meta-analyses, whereas 42% (8 of 19) primary studies were included by only a single meta-analysis. Substantial clinical heterogeneity existed regarding patient characteristics, surgical techniques, and outcome definitions. Conclusions: This systematic review of meta-analyses identified critically low methodological quality, high primary study overlap, and substantial clinical heterogeneity in the existing evidence comparing pelvic fixation techniques for ASD. While published meta-analyses generally favor S2AI screws, these significant limitations prevent drawing definitive conclusions about superiority. Future research should prioritize high-quality prospective studies with standardized reporting to generate more reliable evidence for improving surgical outcomes in ASD management. Full article

Background: Metabolic syndrome, a clinical condition defined by central obesity, impaired glucose regulation, elevated blood pressure, hypertriglyceridemia, and low high-density lipoprotein cholesterol across the lifespan, is now a major public health issue typically managed with lifestyle, behavioral, and dietary recommendations. However, “one-size-fits-all” recommendations often yield modest, heterogeneous responses and poor long-term adherence, creating a clinical need for more targeted and implementable preventive and therapeutic strategies. Objective: To synthesize evidence on how the gut microbiome can inform precision nutrition and exercise approaches for metabolic syndrome prevention and management, and to evaluate readiness for clinical translation. Key findings: The gut microbiome may influence cardiometabolic risk through microbe-derived metabolites and pathways involving short-chain fatty acids, bile acid signaling, gut barrier integrity, and low-grade systemic inflammation. Diet quality (e.g., Mediterranean-style patterns, higher fermentable fiber, or lower ultra-processed food intake) consistently relates to more favorable microbial functions, and intervention studies show that high-fiber/prebiotic strategies can improve glycemic control alongside microbiome shifts. Physical exercise can also modulate microbial diversity and metabolic outputs, although effects are typically subtle and may depend on baseline adiposity and sustained adherence. Emerging “microbiome-informed” personalization, especially algorithms predicting postprandial glycemic responses, has improved short-term glycemic outcomes compared with standard advice in controlled trials. Targeted microbiome-directed approaches (e.g., Akkermansia muciniphila-based supplementation and fecal microbiota transplantation) provide proof-of-concept signals, but durability and scalability remain key limitations. Conclusions: Microbiome-informed personalization is a promising next step beyond generic guidelines, with potential to improve adherence and durable metabolic outcomes. Clinical implementation will require standardized measurement, rigorous external validation on clinically meaningful endpoints, interpretable decision support, and equity-focused evaluation across diverse populations. Full article

Any industrial application that uses convolutional neural networks (CNNs) requires initial data and resources in order to train the models. However, the selection of models must be appropriate to the quality and quantity of the available data and computational resources. This study analyses the influence of data quantity and quality on the performance of CNN models of different complexity. Image preprocessing and image transformation data augmentation techniques are applied to generate different amounts of synthetic data with which to train the aforementioned models, shedding light on the following question: does the quality and quantity of the data or the depth of the model have more influence? Different experiments are performed using the Northeastern University (NEU) Steel Surface Defects Database, which contains surface defects found in hot-rolled steel. After analyzing the results, the authors conclude that data quality and quantity have a much greater influence than model choice. As resources and time are often limited in industry and the ultimate goal is to maximize profit by increasing efficiency, the authors encourage researchers to carefully consider the industrial application at hand and analyze the available data and resources before selecting CNN models. Full article

The Santa Elena Peninsula has experienced local subduction earthquakes in 1901 (7.7 Mw) and 1933 (6.9 Mw), during which local ground conditions, including deposits of longshore-current sediments, paleo-lagoon or marsh, sandspit, and ancient tidal channel sediments, exhibited various coseismic deformation behaviors in Quaternary soils of inferior geotechnical quality. This study shows that geophysical profiles from seismic refraction and shear-wave velocities are correlated with stratigraphic data from sedimentary sequences obtained from slope cutting and geotechnical drilling. This database is used to create a comprehensive map to describe the lithological units of Salinas’ urban geology. The thickness of the Tertiary–Quaternary sedimentary sequences and the depth to the bedrock of the Piñon and Cayo geological formations determine the periods of sites in these stratigraphic sequences, which range from 0.3 to 1.5 s. This study provides the first geotechnical zoning map for the city of Salinas at a scale of 1:25,000, which is a technical requirement of the Ecuadorian construction standard. This geotechnical zoning information is essential for appropriate land management in Salinas and its neighboring cities, La Libertad and Santa Elena, as well as for outlining municipal restrictions on future construction. Full article

Brown seaweeds are marine bioresources rich in bioactive compounds such as carbohydrates, proteins, pigments, fatty acids, polyphenols, vitamins, and minerals. Among these substances, brown algae-derived polysaccharides (alginate, fucoidan, and laminarin) have promising industrial prospects owing to their distinctive structural features and diverse biological activities. Consequently, processing technologies have advanced substantially to address industrial requirements for biopolymer quality, cost-effectiveness, and sustainability. Over the years, significant progress has been made in developing various advanced methods for the sake of extracting, purifying, and structurally characterizing polysaccharides. Aside from that, numerous studies reported their broad spectrum of biological activities, such as antioxidant, anti-inflammatory, anticoagulant, and antimicrobial properties. Furthermore, these substances have various industrial, pharmaceutical, bioenergy, food, and other biotechnology applications. The present review systematically outlines the brown algae-derived polysaccharides treatment process, covering the entire value chain from seaweed harvesting to advanced extraction methods, while highlighting their biological activities and industrial potential as well. Full article

In order to screen high-quality processed tomato germplasm resources, the present research measured the content of quality indicators—lycopene, soluble solids, total acidity, total sugar, and vitamin C—in mature fruits of 113 processed tomato high-generation inbred lines. Comprehensive evaluations of germplasm quality were conducted through genetic diversity analysis, correlation analysis, principal component analysis, and cluster analysis. The results indicated that the variability of the five quality traits in the materials under test was relatively high, with a range of variation from 12.21% to 39.04%. Total sugar exhibited the greatest variation, while soluble solids content showed the least variation. The genetic diversity index ranged from 1.899 to 2.064, with total sugar, vitamin C, and lycopene showing high genetic variation. Soluble solids content was significantly positively correlated with lycopene, total sugar, and total acidity, while lycopene content was significantly positively correlated with total sugar. Vitamin C showed weaker correlations with other traits, but exhibited a significant negative correlation with total sugar. Total acidity had relatively simple correlations with other traits, being significantly correlated only with soluble solids. The three principal components extracted from the principal component analysis all had eigenvalues above 0.8%, contributing to a cumulative contribution rate of 77.435%. Through cluster analysis, the tested materials were divided into six major groups at an Euclidean distance of 15. Group I serves as candidate materials for breeding varieties with good basic quality and high vitamin C content. Group II stood out in terms of high sugar and lycopene content, suitable for developing tomato sauce or juice products with high vibrancy and sweetness. Group III had a high nutritional value and vibrant color, serving as core germplasm resources for breeding high-end processing-specific varieties. Group IV had high soluble solids content, making it a parent source for improving the viscosity and flavor of sauce tomatoes. Group V was suitable for specific formulations requiring high acidity or as breeding materials for high-acidity characteristics. Group VI had limited processing potential and should be used cautiously in breeding. The comprehensive evaluation results showed that the top five germplasm resources in terms of score were W119, 61, 82, 83, and W144. This study enriched the high-quality processed tomato germplasm resources and provided parental resources for quality breeding of processed tomatoes. Full article

To maintain power system stability and supply quality when integrating doubly fed induction generator (DFIG)-based wind energy conversion systems (DFIG-WECSs), regulators regularly update grid codes specifying low voltage ride-through (LVRT) requirements. This paper presents a systematic literature review (SLR) on the use of STATCOMs to enhance LVRT capability in DFIG-WECSs. Objectives included a structured literature search, bibliographic analysis, thematic synthesis, trend identification, and proposing future research directions. A PRISMA-based methodology guided the review, utilising PRISMA 2020 for Abstracts in the development of the abstract. The final search was conducted on Scopus (31 March 2025). Eligible studies were primary research in English (2009–2014) where STATCOM was central to LVRT enhancement; exclusions included non-English studies, duplicates, reviews, and studies without a STATCOM focus. Quality was assessed using an adapted Critical Appraisal Skills Programme (CASP) tool. No automation or machine learning tools were used. Thirty-eight studies met the criteria and were synthesised under four themes: operational contexts, STATCOM-based schemes, control strategies, and optimisation techniques. Unlike prior reviews, this study critically evaluates merits, limitations, and practical challenges. Trend analysis shows evolution from hardware-based fault survival strategies to advanced optimisation and coordinated control schemes, emphasising holistic grid stability and renewable integration. Identified gaps include cyber-physical security, techno-economic assessments, and multi-objective optimisation. Actionable research directions are proposed. By combining technical evaluation with systematic trend analysis, this review clarifies the state of STATCOM-assisted LVRT strategies and outlines pathways for future innovation in DFIG-WECS integration. Full article

This study aims to assess the energy efficiency of LED luminaires used in public road lighting by comparing manufacturer-declared photometric and electrical parameters with laboratory simulation results. The research also evaluates the performance of these luminaires across various road types and installation configurations to determine compliance with national and international standards. Eleven LED luminaires were tested using a rotating mirror goniophotometer in an ISO/IEC 17025-accredited laboratory. Simulations were conducted using Dialux Evo software across six road types (M1–M6) and three installation configurations (unilateral, bilateral, and staggered). Key parameters analyzed included brog (L_m), overall uniformity (U₀), longitudinal uniformity (U_l), luminous efficacy (lm/W), power factor, and total harmonic distortion (THD) in voltage and current. Discrepancies were found between manufacturer-declared and simulation results, especially in higher-class roads (M1–M3), where up to 28.57% of luminaires failed to meet the minimum luminance requirements when tested. The study highlights the importance of validating manufacturer specifications through accredited laboratory testing. Overall, LED technology improves energy efficiency in public lighting, and inconsistencies in the power factor and luminance performance suggest the need for stricter regulatory oversight and more rigorous quality control. Simulation tools like Dialux Evo prove essential for optimizing lighting designs tailored to specific road types and traffic conditions. Full article