Search Results (13)

With the advancement in the “dual carbon” strategy and the integration of high proportions of renewable energy sources, AC/DC ultra-high-power grids are facing new security challenges such as commutation failure and multi-infeed coupling effects. Fault diagnosis, as an important tool for assisting power grid dispatching, is essential for maintaining the grid’s long-term stable operation. Traditional fault diagnosis methods encounter challenges such as limited samples and data quality issues under complex operating conditions. To overcome these problems, this study proposes a fault sample data enhancement method based on the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). Firstly, a simulation model of the AC/DC hybrid system is constructed to obtain the original fault sample data. Then, through the adoption of the Wasserstein distance measure and the gradient penalty strategy, an improved WGAN-GP architecture suitable for feature learning of the AC/DC hybrid system is designed. Finally, by comparing the fault diagnosis performance of different data models, the proposed method achieves up to 100% accuracy on certain fault types and improves the average accuracy by 6.3% compared to SMOTE and vanilla GAN, particularly under limited-sample conditions. These results confirm that the proposed approach can effectively extract fault characteristics from complex fault data. Full article

Background: Iron deficiency is a prevalent issue among elite athletes, particularly in endurance-based sports like football, where optimal iron status is crucial for aerobic capacity and performance. Despite the well-documented role of iron in oxygen transport and energy metabolism, the interplay between genetic polymorphisms, biochemical markers, and iron supplementation remains poorly understood. This study aimed to investigate the relationship between genetic polymorphisms and iron status in professional football players, assess the impact of iron supplementation on athletic performance, and develop a predictive model for iron supplementation based on genetic and biochemical profiles. Methods: A longitudinal study was conducted over three seasons (2021–2024) with 48 male professional football players. Participants underwent genotyping for polymorphisms in ACE (rs4646994), ACTN3 (rs1815739), AMPD1 (rs17602729), CKM (rs8111989), HFE (rs1799945), and MLCK (rs2700352, rs28497577). Biochemical markers (ferritin, haemoglobin, haematocrit, serum iron) and performance metrics (GPS-derived data) were monitored. Iron supplementation (105 mg/day ferrous sulphate) was administered to players with ferritin <30 ng/mL. A Total Genotype Score (TGS) was calculated to evaluate genetic predisposition. Results: Players with “optimal” genotypes (ACE DD, ACTN3 CC, AMPD1 CC, HFE GC) required less iron supplementation (TGS = 51.25 vs. 41.32 a.u.; p = 0.013) and exhibited better performance metrics. Iron supplementation significantly improved haemoglobin and haematocrit in deficient players (p < 0.05). The TGS predicted supplementation need (AUC = 0.711; p = 0.023), with a threshold of 46.42 a.u. (OR = 5.23, 95% CI: 1.336–14.362; p = 0.017 for non-supplemented players). Furthermore, performance data revealed that iron-supplemented players had significantly lower competition time (1128.40 vs. 1972.84 min; p = 0.003), total distance covered (128,129.42 vs. 218,556.64 m; p = 0.005), and high-speed running in the 18–21 km/h (7.58 vs. 10.36 m/min; p = 0.007) and 21–24 km/h (4.43 vs. 6.13 m/min; p = 0.010) speed zones. They also started fewer matches (11.50 vs. 21.59; p < 0.001). Conclusions: Genetic profile combined with biochemical monitoring effectively predicts iron supplementation needs in athletes. Personalized nutrition strategies, guided by TGS, can optimize iron status and enhance performance in elite football players. This approach bridges a critical gap in sports science, offering a framework for precision nutrition in athletics. Full article

The food industry generates considerable byproducts that are often discarded and have high contents of usable bioactive compounds. The aim of this study was to characterize the byproducts of camu-camu (Myrciaria dubia) (shell and seed) and guava (Psidium guajava) (shell) production. The extraction and stabilization of the bioactive compounds of camu-camu and guava were also optimized. The variables of ultrasound-assisted extraction (UAE) (shaking time, sonication time and volume–mass ratio) and mechanical shaking-based extraction (MS) (shaking speed, volume–mass ratio and shaking time) were optimized with the surface response method and a Box–Behnken design. The responses studied were total phenolic content (TPC) and antioxidant capacity (AC) evaluated by the degradation of the radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) technique and by the ferric reducing antioxidant powder (FRAP) test. For ultrasound-assisted extraction, the optimal sonication time was 15 min for both the M. dubia and P. guajava shells, and the volume–mass ratios were 50 mL/g for the M. dubia shell and 60 mL/g for the P. guajava shell. However, for M. dubia seeds, there was an agitation time of 3 h, a sonication time of 4.4 min and a volume–mass ratio of 50 mL/g. During extraction by mechanical stirring, the optimal volume–mass ratio for both M. dubia seeds and P. guajava shells was 60 mL/g, while for M. dubia shells, it was 50 mL/g. For the shells and seeds of M. dubia and the shells of P. guajava, the optimal stirring times were 2, 6.4 and 7.7 h, respectively, and the optimal stirring speeds were 172.2, 250 and 256.3 rpm, respectively. Under these optimal conditions, the highest total phenolic content (TPC) results were acquired from the cuma-cuma peel (CCP) extract (26.2 mg gallic acid equivalent (GAE)/g sample) obtained by UAE and from guava peel (GP) extract (27.9 mg GAE/g sample) obtained by MS. The optimized models showed that MS was more efficient than UAE for obtaining bioactive compounds from byproducts of M. dubia and P. guajava. However, UAE required much shorter extraction times than MS. In conclusion, the models obtained for the recovery of bioactive compounds could be applied in large-scale industries to fully exploit the byproducts studied. Full article

This study investigated the impact of the June 2015 geomagnetic storms on the Brazilian equatorial and low-latitude ionosphere by analyzing various data sources, including solar wind parameters from the advanced compositional explorer satellite (ACE), global positioning satellite vertical total electron content (GPS-VTEC), geomagnetic data, and validation of the SAMI2 model-VTEC with GPS-VTEC. The effect of geomagnetic disturbances on the Brazilian longitudinal sector was examined by applying multiresolution analysis (MRA) of the maximum overlap discrete wavelet transform (MODWT) to isolate the diurnal component of the disturbance dynamo (Ddyn), DP2 current fluctuations from the ionospheric electric current disturbance (Diono), and semblance cross-correlation wavelet analysis for local phase comparison between the Sq and Diono currents. Our findings revealed that the significant fluctuations in DP2 at the Brazilian equatorial stations (Belem, dip lat: −0.47° and Alta Floresta, dip lat: −3.75°) were influenced by IMF Bz oscillations; the equatorial electrojet also fluctuated in tandem with the DP2 currents, and dayside reconnection generated the field-aligned current that drove the DP2 current system. The short-lived positive ionospheric storm during the main phase on 22 June in the Southern Hemisphere in the Brazilian sector was caused by the interplay between the eastward prompt penetration of the magnetospheric convection electric field and the westward disturbance dynamo electric field. The negative ionospheric storms that occurred during the recovery phase from 23 to 29 June 2015, were attributed to the westward disturbance dynamo electric field, which caused the downward $\mathrm{E} \times \mathrm{B}$ drift of the plasma to a lower height with a high recombination rate. The comparison between the SAMI2 model-VTEC and GPS-VTEC indicates that the SAMI2 model underestimated the VTEC within magnetic latitudes of −9° to −24° in the Brazilian longitudinal sector from 6 to 17 June 2015. However, it demonstrated satisfactory agreement with the GPS-VTEC within magnetic latitudes of −9° to 10° from 8 to 15 June 2015. Conversely, the SAMI2 model overestimated the VTEC between ±10° magnetic latitudes from 16 to 28 June 2015. The most substantial root mean square error (RMSE) values, notably 10.30 and 5.48 TECU, were recorded on 22 and 23 June 2015, coinciding with periods of intense geomagnetic disturbance. Full article

Recently, equipment replacement and maintenance repair and operation (MRO) optimization have substantially increased owing to the aging and deterioration of industrial plants, such as steel-making factories in Korea. Therefore, plant owners are required to quickly review equipment supply contracts, i.e., purchase order (PO) documents, with suppliers and vendors. Currently, there is inconsistency in the time and quality required for the PO document review process by engineers, depending on their manual skills and practice. This study developed a general provisions question-answering model (GPQAM) by combining knowledge graph (KG) and question-answering (QA) techniques to search for semantically connected contract clauses through the definition of relationships between entities during the review of equipment purchase contracts. The PO documents analyzed in this case study were based on one steel-making company’s general provisions (GP). GPQAM is a machine learning (ML)-based model with two sub-models (i.e., KG and QA) that automatically generates the most relevant answers to semantic search questions through a cypher query statement in GP for the PO engineers. First, based on the developed GP lexicon and its classifying taxonomy to be stored in the Neo4j graph database (GDB), the KG sub-model finds the corresponding synonyms and consequently shows GP-related information in a graphic form. Second, the QA sub-model is a function to find and answer contract information within the KG and applies pattern-matching technology based on the Aho–Corasick (AC) algorithm. Third, nodes with the meaning most similar to the question are selected using similarity measurement if a response cannot be extracted through the pattern-matching process. Forty-five pilot test questions were created and applied to the GPQAM model evaluation. The F1 score was 82.8%, indicating that the unsupervised training methods developed in this study could be better applied to a semantic QA process in plant engineering documents, where sufficient training data are limited and bargained. An expert survey of PO practitioners confirmed that the semantic QA capability of GPQAM might be efficient and useful for their work. As the first case of applying KG technology to semantic QA for plant equipment PO contracts, this study might be a meaningful contribution to the steel plant industry and, therefore, extended to construction and engineering contract applications. Full article

Abnormalities in the peripheral immune system are involved in the pathophysiology of fibromyalgia, although their contribution to the painful symptoms remains unknown. Our previous study reported the ability of splenocytes to develop pain-like behavior and an association between the central nervous system (CNS) and splenocytes. Since the spleen is directly innervated by sympathetic nerves, this study aimed to examine whether adrenergic receptors are necessary for pain development or maintenance using an acid saline-induced generalized pain (AcGP) model (an experimental model of fibromyalgia) and whether the activation of these receptors is also essential for pain reproduction by the adoptive transfer of AcGP splenocytes. The administration of selective β2-blockers, including one with only peripheral action, prevented the development but did not reverse the maintenance of pain-like behavior in acid saline-treated C57BL/6J mice. Neither a selective α1-blocker nor an anticholinergic drug affects the development of pain-like behavior. Furthermore, β2-blockade in donor AcGP mice eliminated pain reproduction in recipient mice injected with AcGP splenocytes. These results suggest that peripheral β2-adrenergic receptors play an important role in the efferent pathway from the CNS to splenocytes in pain development. Full article

The polypeptide TFF3 belongs to the trefoil factor family (TFF) of lectins. TFF3 is typically secreted from mucous epithelia together with mucins. Both intestinal and salivary TFF3 mainly exist as disulfide-linked heterodimers with IgG Fc binding protein (FCGBP). Here, we investigated bronchial tissue specimens, bronchial secretions, and bronchoalveolar lavage (BAL) fluid from patients with a chronic obstructive pulmonary disease (COPD) background by fast protein liquid chromatography and proteomics. For the first time, we identified different molecular forms of TFF3 in the lung. The high-molecular mass form represents TFF3-FCGBP oligomers, whereas the low-molecular mass forms are homodimeric and monomeric TFF3 with possibly anti-apoptotic activities. In addition, disulfide-linked TFF3 heterodimers with an M_r of about 60k and 30k were detected in both bronchial secretions and BAL fluid. In these liquids, TFF3 is partly N-terminally truncated probably by neutrophil elastase cleavage. TFF3-FCGBP is likely involved in the mucosal innate immune defense against microbial infections. We discuss a hypothetical model how TFF3 might control FCGBP oligomerization. Furthermore, we did not find indications for interactions of TFF3-FCGBP with DMBT1gp³⁴⁰ or the mucin MUC5AC, glycoproteins involved in mucosal innate immunity. Surprisingly, bronchial MUC5AC appeared to be degraded when compared with gastric MUC5AC. Full article

Colorectal cancer therapies have produced promising clinical responses, but tumor cells rapidly develop resistance to these drugs. It has been previously shown that EC19 and EC23, two EC-synthetic retinoids, have single-agent preclinical anticancer activity in colorectal carcinoma. Here, isobologram analysis revealed that they have synergistic cytotoxicity with retinoic acid receptor (RAR) isoform-selective agonistic retinoids such as AC261066 (RARβ2-selective agonist) and CD437 (RARγ-selective agonist) in Caco-2 cells. This synergism was confirmed by calculating the combination index (lower than 1) and the dose reduction index (higher than 1). Flow cytometry of combinatorial IC₅₀ (the concentration causing 50% cell death) confirmed the cell cycle arrest at the SubG₀-G₁ phase with potentiated apoptotic and necrotic effects. The reported synergistic anticancer activity can be attributed to their ability to reduce the expression of ATP-binding cassette (ABC) transporters including P-glycoprotein (P-gp1), breast cancer resistance protein (BCRP) and multi-drug resistance-associated protein-1 (MRP1) and Heat Shock Protein 70 (Hsp70). This adds up to the apoptosis-promoting activity of EC19 and EC23, as shown by the increased Caspase-3/7 activities and DNA fragmentation leading to DNA double-strand breaks. This study sheds the light on the possible use of EC-synthetic retinoids in the rescue of multi-drug resistance in colorectal cancer using Caco-2 as a model and suggests new promising combinations between different synthetic retinoids. The current in vitro results pave the way for future studies on these compounds as possible cures for colorectal carcinoma. Full article

Background: Heritability explains 45-82% of muscle mass and strength variation, yet polygenic models for muscle phenotypes in older women are scarce. Therefore, the objective of the present study was to (1) assess if total genotype predisposition score (GPS_TOTAL) for a set of polymorphisms differed between older women with low and high muscle mass, and (2) utilise a data-driven GPS (GPS_DD) to predict the variance in muscle size and strength-related phenotypes. Methods: In three-hundred 60- to 91-year-old Caucasian women (70.7 ± 5.7 years), skeletal muscle mass, biceps brachii thickness, vastus lateralis anatomical cross-sectional area (VL_ACSA), hand grip strength (HGS), and elbow flexion (MVC_EF) and knee extension (MVC_KE) maximum voluntary contraction were measured. Participants were classified as having low muscle mass if the skeletal muscle index (SMI) < 6.76 kg/m² or relative skeletal muscle mass (%SMM_r) < 22.1%. Genotyping was completed for 24 single-nucleotide polymorphisms (SNPs). GPS_TOTAL was calculated from 23 SNPs and compared between the low and high muscle mass groups. A GPS_DD was performed to identify the association of SNPs with other skeletal muscle phenotypes. Results: There was no significant difference in GPS_TOTAL between low and high muscle mass groups, irrespective of classification based on SMI or %SMM_r. The GPS_DD model, using 23 selected SNPs, revealed that 13 SNPs were associated with at least one skeletal muscle phenotype: HIF1A rs11549465 was associated with four phenotypes and, in descending number of phenotype associations, ACE rs4341 with three; PTK2 rs7460 and CNTFR rs2070802 with two; and MTHFR rs17421511, ACVR1B rs10783485, CNTF rs1800169, MTHFR rs1801131, MTHFR rs1537516, TRHR rs7832552, MSTN rs1805086, COL1A1 rs1800012, and FTO rs9939609 with one phenotype. The GPS_DD with age included as a predictor variable explained 1.7% variance of biceps brachii thickness, 12.5% of VL_ACSA, 19.0% of HGS, 8.2% of MVC_EF, and 9.6% of MVC_KE. Conclusions: In older women, GPS_TOTAL did not differ between low and high muscle mass groups. However, GPS_DD was associated with muscle size and strength phenotypes. Further advancement of polygenic models to understand skeletal muscle function during ageing might become useful in targeting interventions towards older adults most likely to lose physical independence. Full article

Polyphenols have become a research target due to their antioxidant, anti-inflammatory and antimicrobial activity. Obtention via extraction from natural sources includes the revalorization of food wastes such as grape pomace (GP) or apple pomace (AP). In this work, GP and AP were submitted to a liquid–solid extraction using different solvents of industrial interest. Process kinetics were studied measuring the total phenolic content (TPC) and antioxidant capacity (AC), while the extraction liquor composition was analyzed employing chromatographic methods. Extraction processes using water-solvent mixtures stood out as the better options, with a particular preference for water 30%–ethanol 70% (v/v) at 90 °C, a mixture that quickly extracts up to 68.46 mg GAE/gds (Gallic Acid Equivalent per gram dry solid) and 122.67 TEAC/gds (TROLOX equivalent antioxidant capacity per gram dry solid) in case of GP, while ethylene water 10%–ethylene glycol 90% (v/v) at 70 °C allows to reach 27.19 mg GAE/gds and 27.45 TEAC/gds, in the case of AP. These extraction processes can be well-described by a second-order kinetic model that includes a solubility-related parameter for the first and fast-washing and two parameters for the slow mass transfer controlled second extraction phase. AP liquors were found to be rich in quercetin with different sugar moieties and GP extracts highlighted flavonols, cinnamic acids, and anthocyanins. Therefore, using identical extraction conditions for AP and GP and a comparative kinetic analysis of TPC and AC results for the first time, we concluded that ethanol/water mixtures are adequate solvents for polyphenols extraction due to their high efficiency and environmentally benign nature. Full article

Every year approximately 1.24 million people are diagnosed with blood cancer. While the rate increases each year, the availability of data for each kind of blood cancer remains scarce. It is essential to produce enough data for each blood cell type obtained from bone marrow aspirate smears to diagnose rare types of cancer. Generating data would help easy and quick diagnosis, which are the most critical factors in cancer. Generative adversarial networks (GAN) are the latest emerging framework for generating synthetic images and time-series data. This paper takes microscopic cell images, preprocesses them, and uses a hybrid GAN architecture to generate synthetic images of the cell types containing fewer data. We prepared a single dataset with expert intervention by combining images from three different sources. The final dataset consists of 12 cell types and has 33,177 microscopic cell images. We use the discriminator architecture of auxiliary classifier GAN (AC-GAN) and combine it with the Wasserstein GAN with gradient penalty model (WGAN-GP). We name our model as WGAN-GP-AC. The discriminator in our proposed model works to identify real and generated images and classify every image with a cell type. We provide experimental results demonstrating that our proposed model performs better than existing individual and hybrid GAN models in generating microscopic cell images. We use the generated synthetic data with classification models, and the results prove that the classification rate increases significantly. Classification models achieved 0.95 precision and 0.96 recall value for synthetic data, which is higher than the original, augmented, or combined datasets. Full article

Groundwater is one of the most important natural resources, as it regulates the earth’s hydrological system. The Damghan sedimentary plain area, located in the region of a semi-arid climate of Iran, has very critical conditions of groundwater due to massive pressure on it and is in need of robust models for identifying the groundwater potential zones (GWPZ). The main goal of the current research is to prepare a groundwater potentiality map (GWPM) considering the probabilistic, machine learning, data mining, and multi-criteria decision analysis (MCDA) approaches. For this purpose, 80 wells collected from the Iranian groundwater resource department and field investigation with global positioning system (GPS), have been selected randomly and considered as the groundwater inventory datasets. Out of 80 wells, 56 (70%) wells have been brought into play for modeling and 24 (30%) for validation purposes. Elevation, slope, aspect, convergence index (CI), rainfall, drainage density (Dd), distance to river, distance to fault, distance to road, lithology, soil type, land use/land cover (LU/LC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic position index (TPI), and stream power index (SPI) have been used for modeling purpose. The area under the receiver operating characteristic (AUROC), sensitivity (SE), specificity (SP), accuracy (AC), mean absolute error (MAE), and root mean square error (RMSE) are used for checking the goodness-of-fit and prediction accuracy of approaches to compare their performance. In addition, the influence of groundwater determining factors (GWDFs) on groundwater occurrence was evaluated by performing a sensitivity analysis model. The GWPMs, produced by technique for order preference by similarity to ideal solution (TOPSIS), random forest (RF), binary logistic regression (BLR), weight of evidence (WoE) and support vector machine (SVM) have been classified into four categories, i.e., low, medium, high and very high groundwater potentiality with the help of the natural break classification methods in the GIS environment. The very high groundwater potentiality class is covered 15.09% for TOPSIS, 15.46% for WoE, 25.26% for RF, 15.47% for BLR, and 18.74% for SVM of the entire plain area. Based on sensitivity analysis, distance from river, and drainage density represent significantly effects on the groundwater occurrence. validation results show that the BLR model with best prediction accuracy and goodness-of-fit outperforms the other five models. Although, all models have very good performance in modeling of groundwater potential. Results of seed cell area index model that used for checking accuracy classification of models show that all models have suitable performance. Therefore, these are promising models that can be applied for the GWPZs identification, which will help for some needful action of these areas. Full article

The predicted parts of ultra-rapid orbits are important for (near) real-time Global Navigation Satellite System (GNSS) precise applications; and there is little research on GPS/GLONASS/BDS/Galileo/QZSS five-system ultra-rapid precise orbit determination; based on the one-step method and double-difference observation model. However; the successful development of a software platform for solving five-system ultra-rapid orbits is the basis of determining and analyzing these orbits. Besides this; the different observation models and processing strategies facilitate to validate the reliability of the various ultra-rapid orbits. In this contribution; this paper derives the double-difference observation model of five-system ultra-rapid precise orbit determination; based on a one-step method; and embeds this method and model into Bernese v5.2; thereby forming a new prototype software platform. For validation purposes; 31 days of real tracking data; collected from 130 globally-distributed International GNSS Service (IGS) multi-GNSS Experiment (MGEX) stations; are used to determine a five-system ultra-rapid precise orbit. The performance of the software platform is evaluated by analysis of the orbit discontinuities at day boundaries and by comparing the consistency with the MGEX orbits from the Deutsches GeoForschungsZentrum (GFZ); between the results of this new prototype software platform and the ultra-rapid orbit provided by the International GNSS Monitoring and Assessment System (iGMAS) analysis center (AC) at the Institute of Geodesy and Geophysics (IGG). The test results show that the average standard deviations of orbit discontinuities in the three-dimension direction are 0.022; 0.031; 0.139; 0.064; 0.028; and 0.465 m for GPS; GLONASS; BDS Inclined Geosynchronous Orbit (IGSO); BDS Mid-Earth Orbit (MEO); Galileo; and QZSS satellites; respectively. In addition; the preliminary results of the new prototype software platform show that the consistency of this platform has been significantly improved compared to the software package of the IGGAC. Full article