Search Results (7,551)

With the rapid development of high-resolution satellite remote sensing technology, wind turbine detection based on remote sensing imagery has emerged as a crucial research area in renewable energy. However, accurate identification of wind turbines remains challenging due to complex geographical backgrounds and their typical appearance as small objects in images, where limited features and background interference hinder detection performance. To address these issues, this paper proposes CGA-YOLO, a specialized network for detecting small targets in high-resolution remote sensing images, and constructs the SDWT dataset, containing Gaofen-2 imagery covering various terrains in Shandong Province, China. The network incorporates three key enhancements: dynamic convolution improves multi-scale feature representation for precise localization; the Convolutional Block Attention Module (CBAM) enhances feature convergence through channel and spatial attention mechanisms; and GhostBottleneck maintains high-resolution details while strengthening feature channels for small targets. Experimental results demonstrate that CGA-YOLO achieves an F1-score of 0.93 and an mAP50 of 0.938 on the SDWT dataset, and obtains an mAP50 of 0.9033 on both RSOD and VEDAI public datasets. CGA-YOLO establishes its superior accuracy over multiple mainstream detection models under identical experimental conditions, confirming its potential as a reliable technical solution for accurate wind turbine identification in complex environments. Full article

Normalization is a critical step in Multiple-Criteria Decision Analysis (MCDA) because it transforms heterogeneous criterion values into comparable information. This study examines normalization techniques through the lens of entropy, highlighting how criterion data structure shapes normalization behavior and ranking stability within TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Seven widely used normalization procedures are analyzed regarding mathematical properties, sensitivity to extreme values, treatment of benefit and cost criteria, and rank reversal. Normalization is treated as a source of uncertainty in MCDA outcomes, as different schemes can produce divergent rankings under identical decision settings. Shannon entropy is employed as a descriptive measure of information dispersion and structural uncertainty, capturing the heterogeneity and discriminatory potential of criteria rather than serving as a weighting mechanism. An illustrative experiment with ten alternatives and four criteria (two high-entropy, two low-entropy) demonstrates how entropy mediates normalization effects. Seven normalization schemes are examined, including vector, max, linear Sum, and max–min procedures. For vector, max, and linear sum, cost-type criteria are treated using either linear inversion or reciprocal transformation, whereas max–min is implemented as a single method. This design separates the choice of normalization form from the choice of cost-criteria transformation, allowing a cleaner identification of their respective contributions to ranking variability. The analysis shows that normalization choice alone can cause substantial differences in preference values and rankings. High-entropy criteria tend to yield stable rankings, whereas low-entropy criteria amplify sensitivity, especially with extreme or cost-type data. These findings position entropy as a key mediator linking data structure with normalization-induced ranking variability and highlight the need to consider entropy explicitly when selecting normalization procedures. Finally, a practical entropy-based method for choosing normalization techniques is introduced to enhance methodological transparency and ranking robustness in MCDA. Full article

This critical review explores the transformative impact of artificial intelligence (AI), particularly machine learning (ML) and computer vision (CV), on scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) for metallic microstructure analysis, spanning research from 2010 to 2025. It critically evaluates how AI techniques balance automation, accuracy, and scalability, analysing why certain methods (e.g., Vision Transformers for complex microstructures) excel in specific contexts and how trade-offs in data availability, computational resources, and interpretability shape their adoption. The review examines AI-driven techniques, including semantic segmentation, object detection, and instance segmentation, which automate the identification and characterisation of microstructural features, defects, and inclusions, achieving enhanced accuracy, efficiency, and reproducibility compared to traditional manual methods. It introduces the Microstructure Analysis Spectrum, a novel framework categorising techniques by task complexity and scalability, providing a new lens to understand AI’s role in materials science. The paper also evaluates AI’s role in chemical composition analysis and predictive modelling, facilitating rapid forecasts of mechanical properties such as hardness and fracture strain. Practical applications in steelmaking (e.g., automated inclusion characterisation) and case studies on high-entropy alloys and additively manufactured metals underscore AI’s benefits, including reduced analysis time and improved quality control. Extending prior reviews, this work incorporates recent advancements like Vision Transformers, 3D Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Key challenges—data scarcity, model interpretability, and computational demands—are critically analysed, with representative trade-offs from the literature highlighted (e.g., GANs can substantially augment effective dataset size through synthetic data generation, typically at the cost of significantly increased training time). Full article

In the framework of high temperature components, the need to evaluate the accumulated creep damage during service life is fundamental to extend the life of components which are currently deemed as scrap as per design intent. Thus, the life assessment of Ni-based superalloys could be performed in relation to the accumulated creep deformation which represents the limiting factor for serviced components. Despite the different microstructural changes that occur in service life, this work focuses on the possibility to evaluate the material strain by means of electron backscattered diffraction (EBSD). The key point is the identification of the correlation between geometrically necessary dislocation (GND) density derived from EBSD analyses and the reached creep strain for a single crystal Ni-based superalloy. However, the results of GND density are affected by the settings’ parameters adopted to perform the analysis by the magnification level and the step size. These two parameters have been optimized by analyzing specimens from interrupted creep tests at strain levels between 0.5% and 10%, in the temperature range between 850 °C and 1000 °C. Full article

The technological properties of durum wheat grain are determined by prolamins (gliadins and glutenins). Information on the allelic composition of key loci remains incomplete despite existing global studies examining prolamin variability. This highlighted the need to study these traits in durum wheat in Kazakhstan. The effects of specific gliadin components with high- and low-molecular-weight glutenin fractions on gluten quality are also not fully clarified. This study aimed to characterise allelic diversity at prolamin-coding loci and evaluate associated grain quality traits. Using native and denaturing SDS-electrophoresis, 181 tetraploid wheat accessions from Kazakhstan, an International germplasm collection, and 26 breeding lines were analysed for allelic variation and associations with protein content, gluten content, gluten index, and SDS-sedimentation. The γ45 gliadin component and Glu-A3a allele were positively associated with SDS-sedimentation and gluten index, while Glu-B3b had a negative effect. Distinct prolamin profiles were observed among accessions from different ecological and geographical locations. These results support the selection of superior durum wheat genotypes and enable the identification of favourable allele combinations at the Gli-1, Gli-2, Glu-1, and Glu-3 loci in cultivars from Kazakhstan. Comparison with global tetraploid wheat germplasm collections demonstrates unique genetic diversity in genotypes, providing a valuable basis for breeding programs aimed at improving grain and gluten quality in durum wheat in Kazakhstan and Central Asian countries. Full article

A key challenge in automated marigold harvesting lies in the accurate identification of picking points under complex environmental conditions, such as dense shading and intense illumination. To tackle this problem, this research proposes a lightweight instance segmentation model combined with a harvest position estimation method. Based on the YOLOv11n-seg segmentation framework, we develop a lightweight PDS-YOLO model through two key improvements: (1) structural pruning of the base model to reduce its parameter count, (2) incorporation of a Channel-wise Distillation (CWD)-based feature distillation method to compensate for the accuracy loss caused by pruning. The resulting lightweight segmentation model achieves a size of only 1.3 MB (22.8% of the base model) and a computational cost of 5 GFLOPs (49.02% of the base model). At the same time, it maintains high segmentation performance, with a precision of 93.6% and a mean average precision (mAP) of 96.7% for marigold segmentation. Furthermore, the proposed model demonstrates enhanced robustness under challenging scenarios including strong lighting, cloudy weather, and occlusion, improving the recall rate by 1.1% over the base model. Based on the segmentation results, a method for estimating marigold harvest positions using 3D point clouds is proposed. Fitting and deflection angle experiments confirm that the fitting errors are constrained within 3–12 mm, which lies within an acceptable range for automated harvesting. These results validate the capability of the proposed approach to accurately locate marigold harvest positions under top-down viewing conditions. The lightweight segmentation network and harvest position estimation method presented in this work offer effective technical support for selective harvesting of marigolds. Full article

Chloroplasts are key organelles in plants that carry out photosynthesis, convert light energy into chemical energy, and synthesize organic compounds. In this study, a stably heritable chlorophyll-deficient mutant was screened from the ethyl methanesulfonate-induced mutation library of Wuyunjing 21 (WYJ21). This mutant was designated as chlorophyll deficient 6 (cde6). The cde6 mutant exhibits a low chlorophyll content, photosynthetic defects, an impaired chloroplast structure, a significant reduction in the number of stacked thylakoid layers, and a yellow-green leaf phenotype in the early tillering stage. Through MutMap analysis, it was found that the cde6 mutant harbors a single-base mutation (T→A) in the LOC_Os07g38300 gene. This mutation results in an amino acid substitution from valine (Val) to aspartic acid (Asp) in the encoded protein, thereby affecting the protein’s structure and function. The mutation of CDE6 leads to decreased expression of genes related to chloroplast development and chlorophyll biosynthesis. Further studies revealed that the CDE6, a potential chloroplast ribosome recycle factor, leads to high temperature sensitivity in rice when mutated. As high-temperature stress is a primary constraint to global rice productivity, the identification of CDE6 provides a genetic target for improving thermotolerance. In conclusion, these findings demonstrate that CDE6 plays a crucial role in chloroplast biogenesis and provide new insights into its regulatory function in high-temperature tolerance. Full article

Background: Rowing performance in youth athletes is strongly influenced by anthropometric characteristics, body composition, and limb proportions; however, the combined contribution of these factors across developmental stages remains insufficiently understood. This study investigated the relationships between key anthropometric variables and ergometer performance in a multinational cohort of young rowers. Methods: A total of 194 athletes (48 females, 146 males) from ten countries participated. Based on age and sex, participants were categorized into junior female (JF), junior male (JM), adult female (AF), and adult male (AM) groups. Body height, body mass, body fat (F%), relative muscle mass (M%), limb lengths, and body surface area (BSA) were measured. Rowing performance was assessed via maximal 2000 m ergometer trials. Results: Males outperformed females across all age groups (p < 0.001). Performance showed strong positive correlations with body height (r = 0.673, p = 0.003), body mass (r = 0.724, p = 0.005), arm span (r = 0.681, p = 0.002), lower-limb length (r = 0.394, p = 0.004), relative muscle mass (39.9 ± 5.2%; r = 0.531, p < 0.001), and especially BSA (1.94 ± 0.19 m²; r = 0.739, p < 0.001). Relative body fat was negatively associated with performance (17.6 ± 6.9%; r = −0.465, p < 0.001). Conclusions: Findings indicate that rowing performance in youth athletes reflects multidimensional anthropometric configurations rather than isolated traits, characterized primarily by the combined contribution of body surface area, relative muscle mass, and segmental body dimensions. From a practical perspective, higher-performing athletes typically exhibited body surface area values approaching or exceeding ~1.90 m² and relative muscle mass above ~40%, suggesting these ranges as indicative reference benchmarks rather than fixed selection thresholds. Integrating anthropometric profiling with physiological assessment may enhance early talent identification and support individualized training strategies in competitive youth rowing. Full article

Background: Head and neck (H&N) cutaneous melanomas have poorer outcomes than melanomas at other sites, yet sentinel lymph node biopsy (SLNB)—a key prognostic tool in clinically node-negative disease—is less frequently performed, particularly outside tertiary centers. We evaluated the feasibility and prognostic relevance of SLNB in a medium-volume regional institution. Methods: We retrospectively reviewed patients with primary H&N cutaneous melanoma who underwent SLNB at the Department of Oral and Maxillofacial Surgery, University of Szeged, between 2010 and 2022. Clinicopathological features, nodal outcomes, recurrence patterns, recurrence-free survival (RFS), and overall survival (OS) were analyzed using Kaplan–Meier methods and univariate Cox regression. Results: Thirty-eight patients underwent SLNB, with a 100% sentinel lymph node identification rate and no major complications. Positive sentinel lymph nodes were identified in 8 patients (21.1%). Two false-negative events occurred, resulting in a false-omission rate of 6.7% and a negative predictive value of 93.3%. SLN-negative patients demonstrated longer RFS and OS, although differences were not statistically significant. Among patients with intermediate-risk melanoma (pT1b–pT3a), 18.5% had a positive SLN. Conclusions: SLNB is a safe and clinically meaningful staging procedure for H&N melanoma in a medium-volume regional center. Sentinel node status provides important prognostic information and supports appropriate patient selection for contemporary adjuvant therapy. Full article

Background and Objectives: Cognitive disorders are a significant health problem in patients undergoing peritoneal dialysis and can profoundly impair both quality of life and treatment outcomes. Early identification of risk factors for the development of cognitive disorders in this population is therefore essential. This study aimed to (1) determine the prevalence of cognitive dysfunction in patients on peritoneal dialysis, (2) examine its association with sociodemographic characteristics, and (3) assess whether anaemia is associated with cognitive dysfunction in these patients. Materials and Methods: A cross-sectional study was conducted in November 2024 at the University Clinical Centre of Vojvodina, Clinic for Nephrology and Clinical Immunology, and included 36 patients on peritoneal dialysis. The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive function, while a structured questionnaire was used to collect sociodemographic data. Anaemia was determined based on haemoglobin levels. Results: Cognitive dysfunction was present in 69.4% of patients on peritoneal dialysis, while anaemia, as indicated by haemoglobin values, was present in 58.3% of the sample. Older age, rural residence, and lower haemoglobin levels were significantly associated with cognitive dysfunction in patients on peritoneal dialysis. Conclusions: Preserved cognitive function is a key prerequisite for the adequate implementation of peritoneal dialysis and for maintaining patients’ quality of life. The findings indicate the need for further research to identify effective strategies for preventing and treating anaemia, a factor associated with cognitive dysfunction in this patient population. Full article

Determining long-term changes in global ice and water storage from satellite gravimetry remains challenging due to the limited temporal coverage of high-resolution missions. Here, we combine Satellite Laser Ranging (SLR) and Gravity Recovery and Climate Experiment (GRACE) data to reconstruct large-scale, non-linear mass variations from 1995 to 2024, extending gravity-based observations into the pre-GRACE era while preserving spatial detail through backward extrapolation. The combined model reveals widespread and statistically significant accelerations in global water and ice mass changes and enables the identification of key turning points in their temporal evolution. Results indicate that in Svalbard, a non-linear transition in ice mass balance occurred in late 2004, followed by a pronounced acceleration of mass loss due to climate warming. Glaciers in the Gulf of Alaska exhibit persistent mass loss with a marked intensification after 2012, while in the Antarctic Peninsula, ice mass loss substantially slowed and a potential trend reversal emerged around 2021. The reconstructed mass anomalies show strong consistency with independent satellite altimetry and climate indicators, including a clear response to the 1997/1998 El Niño event prior to the GRACE mission. These findings demonstrate that integrating SLR with GRACE enables robust detection of non-linear, climate-driven mass redistribution on a global scale and provides a physically consistent extension of satellite gravimetry records beyond the GRACE era. Full article

Human exposure to persistent organic pollutants such as polychlorinated biphenyls (PCB) and brominated flame retardants (BFR), including both legacy and emerging compounds, remains a concern due to their bioaccumulative nature and potential health effects. Comprehensive analytical methods are necessary to monitor these substances in complex biological matrices, such as human serum. A gas chromatography–mass spectrometry (GC-MS) method was developed for the simultaneous determination of 44 analytes, encompassing PCB and a broad spectrum of BFR with diverse physicochemical properties. The extraction procedure and GC-MS parameters were optimized using a design of experiments approach to maximize performance while minimizing analysis time. The method demonstrated high sensitivity, precision, and accuracy, thereby meeting internationally recognized validation criteria for biomonitoring applications. To further ensure analytical reliability, compound confirmation was achieved using gas chromatography–high-resolution mass spectrometry, providing enhanced selectivity and confidence in identification, particularly for low-level analytes. Key advantages of the method include its applicability to analytes with significantly different chemical behaviors and its capacity to quantify a large number of target compounds simultaneously. This makes it a powerful tool for assessing human exposure to both regulated and emerging halogenated contaminants. Full article

Patulin (PAT) is a fatal mycotoxin that exerts serious threats to human and animal health. Biodegradation of PAT is considered to be one of the promising ways for controlling its contamination. In this study, Lactiplantibacillus plantarum 6076 (LP 6076) with reliable removal efficiency on PAT was screened out from three lactic acid bacteria (LAB) strains. It was found that the PAT was eliminated through degradation by LP 6076, and the intracellular proteins played a crucial role in PAT degradation with the induction of PAT. The proteomic analysis showed that the response of LP 6076 to PAT was by a concerted effort to repair DNA damage, in parallel to adaptive changes in cell wall biosynthesis and central metabolism. Eleven differentially expressed proteins with high fold changes were picked out and identified as PAT degradation candidate enzymes. The 3D structures of the candidate enzymes were predicted, and molecular docking between the enzymes and PAT was performed. Five enzymes, including Acetoin utilization AcuB protein (AU), GHKL domain-containing protein (GHLK), Dihydroneopterin aldolase (DA), YdeI/OmpD-associated family protein (YDEL), and Transcription regulator protein (TR), could dock with PAT with lower affinity and shorter distance. Through molecular docking analysis, DA was ultimately identified as a potential key degrading enzyme. The choice of DA was substantiated by its superior combination of strong binding affinity and a productive binding pose with PAT. VAL84 and GLN51 residues of DA were likely the active sites, forming four hydrogen bonds with PAT. Our study could accelerate the commercial application of biodegradation toward PAT decontamination. Full article

Background/Objectives: Dementia represents a growing public health challenge. The WHO Global Action Plan on the Public Health Response to Dementia emphasizes early detection, risk reduction, and innovation as key priorities. Mild Behavioral Impairment (MBI), defined as the emergence of persistent neuropsychiatric symptoms in older individuals, represents a potential marker of early neurodegeneration and possible window for early intervention. This review explores the role of MBI in dementia prevention, mapping current evidence within the WHO Global Action Plan framework. Methods: A comprehensive search was performed in PubMed, Scopus, and the official WHO website, during 1 September 2025–10 November 2025, without time restrictions. Eligible sources included original clinical studies, reviews, and policy documents addressing MBI, dementia prevention, and public health. Data were thematically synthesized according to the seven objectives of WHO: (1) dementia as a public health priority, (2) dementia awareness and friendliness, (3) dementia risk reduction, (4) dementia diagnosis, treatment, care and support, (5) support for dementia carers, (6) information systems for dementia, and (7) dementia research and innovation. Results: Accumulating evidence indicates that MBI assessment can capture early behavioral manifestations of neurodegenerative and other forms of dementia, correlating with fluid, neuroimaging and genetic biomarkers. Integrating MBI screening through the easy-to-administer MBI Checklist (MBI-C) into clinical and community-based care, including telemedicine pathways and research, may enhance early identification and personalized interventions, enrich the pool for clinical trials, and facilitate research in biomarker and therapy. MBI-related research further supports its integration in remote digital monitoring and population-based prevention. Conclusions: Embedding MBI-informed screening and interventions into national dementia strategies aligns with WHO objectives for early, equitable and scalable prevention and brain health. Full article

Background/Objectives: Greater than 50% of surviving very preterm infants are affected by postnatal growth failure and are at high risk of associated development of bronchopulmonary dysplasia (BPD). Given the influence of enteral feeding on growth failure, we aimed to determine the impact of type, volume, and time of introduction of enteral feeds on mitigating the risk of postnatal growth failure and BPD risk. Methods: This was a retrospective chart review of mothers’ own milk (MOM), pooled pasteurized donor human milk (PDHM) feeding, postnatal growth, and BPD severity in preterm infants <33 weeks of gestation admitted to the Children’s Hospital of Richmond at VCU neonatal intensive care unit between 2021 and 2024. Statistical analysis included linear regression with moderation analysis using the Hayes Process model, chi-square tests, linear and multinomial logistic regression, with p-value < 0.05 considered significant. Results: After controlling for the percentage of MOM received at 34 weeks corrected gestational age (cGA), greater severity of BPD was associated with lower infant weight and growth failure, p < 0.001. Early introduction of MOM (3 days of life) and greater volume of MOM showed better linear growth and decreased risk of severe BPD, respectively (p < 0.001). Conclusions: Provision of MOM to preterm infants within 3 days of life was associated with a moderation of the relationship between gestational age and growth velocity, with improved growth velocity trajectory. Preterm infants who received a greater volume of MOM through 34 weeks cGA experienced less severe BPD compared to those fed higher volumes of PDHM. As the incidence of growth failure paralleled the incidence of BPD severity, identification of key MOM components becomes important to address and augment the value of PDHM in the management of preterm infants. Full article