Search Results (281)

With the optimization and advancement of industrial production and manufacturing, the application scenarios of bearings have become increasingly diverse and highly coupled. This complexity poses significant challenges for the extraction of bearing fault features, consequently affecting the accuracy of cross-condition fault diagnosis methods. To improve the extraction and recognition of fault features and enhance the diagnostic accuracy of models across different conditions, this paper proposes a cross-condition bearing diagnosis method. This method, named MCR-KAResNet-TLDAF, is based on image fusion and a residual network that incorporates the Kolmogorov–Arnold representation theorem. Firstly, the one-dimensional vibration signals of the bearing are processed using Markov transition field (MTF), continuous wavelet transform (CWT), and recurrence plot (RP) methods, converting the resulting images to grayscale. These grayscale images are then multiplied by corresponding coefficients and fed into the R, G, and B channels for image fusion. Subsequently, fault features are extracted using a residual network enhanced by the Kolmogorov–Arnold representation theorem. Additionally, a domain adaptation algorithm combining multiple kernel maximum mean discrepancy (MK-MMD) and conditional domain adversarial network with entropy conditioning (CDAN+E) is employed to align the source and target domains, thereby enhancing the model’s cross-condition diagnostic accuracy. The proposed method was experimentally validated on the Case Western Reserve University (CWRU) dataset and the Jiangnan University (JUN) dataset, which include the 6205-2RS JEM SKF, N205, and NU205 bearing models. The method achieved accuracy rates of 99.36% and 99.889% on the two datasets, respectively. Comparative experiments from various perspectives further confirm the superiority and effectiveness of the proposed model. Full article

Precise estimation of daily reference crop evapotranspiration (ET₀) is critical for water resource management and agricultural irrigation optimization worldwide. In China, diverse climatic zones pose challenges for accurate ET₀ prediction. Here, we evaluate the performance of a support vector machine (SVM) and its hybrid models, PSO-SVM and WOA-SVM, utilizing meteorological data spanning 1960–2020. Our study aims to identify a high-precision, low-input ET₀ estimation tool. The findings indicate that the hybrid models, particularly WOA-SVM, demonstrated superior accuracy with R² values ranging from 0.973 to 0.999 and RMSE values between 0.123 and 0.863 mm/d, outperforming the standalone SVM model with R² values of 0.955 to 0.989 and RMSE values of 0.168 to 0.982 mm/d. The standalone SVM model showed relatively lower accuracy with R² values of 0.822 to 0.887 and RMSE values of 0.381 to 1.951 mm/d. Notably, the WOA-SVM model, with R² values of 0.990 to 0.992 and RMSE values of 0.092 to 0.160 mm/d, emerged as the top performer, showcasing the benefits of the whale optimization algorithm in enhancing SVM’s predictive capabilities. The PSO-SVM model also presented improved performance, especially in the temperate continental zone (TCZ), subtropical monsoon region (SMZ), and temperate monsoon zone (TMZ), when using limited meteorological data as the input. The study concludes that the WOA-SVM model is a promising tool for high-precision daily ET₀ estimation with fewer meteorological parameters across the different climatic zones of China. Full article

The presence of cracks reduces egg quality and safety, and can easily cause food safety hazards to consumers. Machine vision-based methods for cracked egg detection have achieved significant success on in-domain egg data. However, the performance of deep learning models usually decreases under practical industrial scenarios, such as the different egg varieties, origins, and environmental changes. Existing researches that rely on improving network structures or increasing training data volumes cannot effectively solve the problem of model performance decline on unknown egg testing data in practical egg production. To address these challenges, a novel and robust detection method is proposed to extract max domain-invariant features to enhance the model performance on unknown test egg data. Firstly, multi-domain egg data are built on different egg origins and acquisition devices. Then, a multi-domain trained strategy is established by using Maximum Mean Discrepancy with Normalized Squared Feature Estimation (NSFE-MMD) to obtain the optimal matching egg training domain. With the NSFE-MMD method, the original deep learning model can be applied without network structure improvements, which reduces the extremely complex tuning process and hyperparameter adjustments. Finally, robust cracked egg detection experiments are carried out on several unknown testing egg domains. The YOLOV5 (You Only Look Once v5) model trained by the proposed multi-domain training method with NSFE-MMD has a detection mAP of 86.6% on the unknown test Domain 4, and the YOLOV8 (You Only Look Once v8) model has a detection mAP of 88.8% on Domain 4, which is an increase of 8% and 4.4% compared to the best performance of models trained on a single domain, and an increase of 4.7% and 3.7% compared to models trained on all domains. In addition, the YOLOV5 model trained by the proposed multi-domain training method has a detection mAP of 87.9% on egg data of the unknown testing Domain 5. The experimental results demonstrate the robustness and effectiveness of the proposed multi-domain training method, which can be more suitable for the large quantity and variety of egg detection production. Full article

The uneven distribution of global navigation satellite system (GNSS) continuous stations in the Yellow River Basin, combined with the sparse distribution of GNSS continuous stations in some regions and the weak far-field load signals, poses challenges in using GNSS vertical displacement data to invert terrestrial water storage changes (TWSCs). To achieve the inversion of water reserves in the Yellow River Basin using unevenly distributed GNSS continuous station data, in this study, we employed the Tikhonov regularization method to invert the terrestrial water storage (TWS) in the Yellow River Basin using vertical displacement data from network engineering and the Crustal Movement Observation Network of China (CMONOC) GNSS continuous stations from 2011 to 2022. In addition, we applied an inverse distance weighting smoothing factor, which was designed to account for the GNSS station distribution density, to smooth the inversion results. Consequently, a gridded product of the TWS in the Yellow River Basin with a spatial resolution of 0.5 degrees on a daily scale was obtained. To validate the effectiveness of the proposed method, a correlation analysis was conducted between the inversion results and the daily TWS from the Global Land Data Assimilation System (GLDAS), yielding a correlation coefficient of 0.68, indicating a strong correlation, which verifies the effectiveness of the method proposed in this paper. Based on the inversion results, we analyzed the spatial–temporal distribution trends and patterns in the Yellow River Basin and found that the average TWS decreased at a rate of 0.027 mm/d from 2011 to 2017, and then increased at a rate of 0.010 mm/d from 2017 to 2022. The TWS decreased from the lower-middle to lower reaches, while it increased from the upper-middle to upper reaches. Furthermore, an attribution analysis of the terrestrial water storage changes in the Yellow River Basin was conducted, and the correlation coefficients between the monthly average water storage changes inverted from the results and the monthly average precipitation, evapotranspiration, and surface temperature (AvgSurfT) from the GLDAS were 0.63, −0.65, and −0.69, respectively. This indicates that precipitation, evapotranspiration, and surface temperature were significant factors affecting the TWSCs in the Yellow River Basin. Full article

We conducted a multicenter, prospective study (EMBRACE) evaluating the real-life effectiveness, safety, and tolerability of eptinezumab (100 mg/300 mg)—a monoclonal antibody targeting the calcitonin-gene-related peptide (anti-CGRP mAb)—in high-frequency episodic migraine (HFEM) or chronic migraine (CM). The primary endpoint was the change in monthly migraine days (MMD) for HFEM or monthly headache days (MHD) for CM at weeks 9–12 compared to baseline. The secondary endpoints included changes in monthly analgesic intake (MAI), Numerical Rating Scale (NRS), Headache Impact Test (HIT-6), Migraine Disability Assessment Scale (MIDAS), Migraine Interictal Burden Scale (MIBS-4), and responder rates. The safety analysis involved 44 subjects; the effectiveness analysis included 26 individuals. Eptinezumab was well-tolerated. In CM patients, eptinezumab significantly reduced MHD (−16.1 ± 9.9, p < 0.001), MAI, NRS, HIT-6, MIDAS, and MIBS-4. In HFEM patients, it significantly reduced NRS, HIT-6, MIDAS, and MIBS-4, though reductions in MMD (−3.3 ± 4.5) and MAI were not statistically significant. Overall, ≥50% and ≥75% response rates were 61.5% and 30.8%, respectively (60% and 30% in non-responders to subcutaneous anti-CGRP mAbs). The clinical change was rated as much or very much improved by 61.0% of the patients. Eptinezumab demonstrated high effectiveness, safety, and tolerability in real-life among hard-to-treat migraine patients with multiple treatment failures, including anti-CGRP mAbs. Full article

Migraine is a leading cause of disability worldwide, yet it remains underrecognized and undertreated, especially in the pediatric and adolescent population. Chronic migraine occurs approximately in 1% of children and adolescents requiring preventive treatment. Topiramate is the only FDA-approved preventative treatment for children older than 12 years of age, but there is conflicting evidence regarding its efficacy. OnabotulinumtoxinA is a known and approved treatment for the management of chronic migraine in people older than 18 years. Several studies examine its role in the pediatric population with positive results; however, the clear-cut benefit is still unclear. OnabotulinumtoxinA seems not only to improve disability scores (PedMIDAS) but also to improve the quality, characteristics, and frequency of migraines in the said population. This systematic review aims to summarize the evidence on the efficacy, dosing, administration, long-term outcomes, and safety of onabotulinumtoxinA in pediatric and adolescent migraine. Eighteen studies met the eligibility criteria and were included in this review. The mean monthly migraine days (MMDs), decreased from of 21.2 days per month to 10.7 after treatment. The reported treatment-related adverse effects were mild and primarily injection site related and ranged from 0% to 47.0%. Thus, this review provides compelling evidence suggesting that OnabotulinumtoxinA may represent a safe and effective preventive treatment option for pediatric migraine. Full article

The Haihe Plain in China is situated in the world’s largest groundwater funnel area, with per capita water resources far below the internationally recognized “extremely water-scarce” standard. To address the issue of water shortage in summer maize-planting areas of the Haihe Plain, we conducted research on the variation of summer maize evapotranspiration using a medium-sized lysimeter. This study aims to provide technical support for water-saving irrigation in summer maize fields. Through path analysis, direct and indirect influencing factors affecting the evapotranspiration of summer maize fields were determined. The results showed that the cumulative evapotranspiration of bare ground and farmland during the entire growth period of summer maize was 173.57 mm and 382.97 mm, respectively, with evapotranspiration intensities of 1.52 mm/d and 3.36 mm/d, respectively. Evapotranspiration during the maturity stage of summer maize was the least, accounting for only 1.82% of total evapotranspiration during the entire growth period. The period from the jointing to milk-ripening stage is when evapotranspiration in maize fields is at its highest. During this period, evapotranspiration in maize fields amounted to 265.58 mm, accounting for 69.35% of total evapotranspiration. The evapotranspiration intensity was 3.59 mm/day, which is 1.34 times higher than that of bare soil. The evapotranspiration intensities during each growth stage were ranked as jointing stage > tasseling-silking stage > seedling stage > milk maturity stage > maturity stage. The daily evapotranspiration of summer maize fields showed a “unimodal” curve with low values in the morning and evening, and high values at noon. Path analysis indicated that daily radiation and maximum daily temperature had the greatest impact on the evapotranspiration of maize fields, with the direct effect of maximum daily temperature being restrictive and the indirect effect being promotive, resulting in an overall promotive effect. Full article

With the increase in uptake of multi-month antiretroviral therapy dispensing (MMD) for children, little is known about consistency of MMD receipt over time and its association with virological outcomes. This analysis aims to assess the uptake of 3-month MMD among children, consistent receipt of MMD after uptake, and clinical outcomes following transition to MMD in 16 health facilities in Gaza and Inhambane Provinces. This is a secondary analysis involving children <15 years living with HIV with clinical visits during the period from September 2019 to August 2020. Of 4383 children, 82% ever received MMD (at least one pickup of a 3-month MMD supply) during the study period but only 40% received it consistently (defined as MMD at every visit during the study period). Consistent MMD was most common among older children and children without indications of clinical instability. Overall viral load (VL) coverage was 40% (733/1851). Consistent MMD was significantly associated with lower odds of having a VL (0.78, 95% CI: 0.64–0.95). In conclusion, while receipt of a multi-month supply was common particularly during the early days of the COVID-19 pandemic, only a minority of children received consistent MMD; however, there is a need to ensure children with fewer visits still receive timely VL monitoring. Full article

Toddlerhood (aged 13~36 months) is a period of dietary transition, with water intake being significantly influenced by parental feeding patterns, cultural traditions, and the availability of beverages and food. Nevertheless, given the lack of applicable data, it is challenging to guide and evaluate the water intake of toddlers in China. In this study, our objectives were to assess the daily total water intake (TWI), evaluate the consumption patterns of various beverages and food sources contributing to the TWI, determine the conformity of participants to the adequate intake (AI) recommendation of water released by the Chinese Nutrition Society, and analyze the various contributors to the daily total energy intake (TEI). The data for the assessment of water and dietary intake were obtained from the cross-sectional dietary intake survey of infants and young children (DSIYC, 2018–2019). A total of 1360 eligible toddlers were recruited in the analysis. The differences in related variables between two age groups were compared by Mann–Whitney U test and Chi-Square test. The potential correlation between water and energy intake was examined utilizing age-adjusted partial correlation. Toddlers consumed a median daily TWI of 1079 mL, with 670 mL (62.3%, r = 0.752) derived from beverages and 393 mL (37.7%, r = 0.716) from foods. Plain water was the primary beverage source, contributing 300 mL (52.2%, r = 0.823), followed by milk and milk derivatives (MMDs) at 291 mL (45.6%, r = 0.595). Notably, only 28.4% of toddlers managed to reach the recommended AI value. Among these, toddlers obtain more water from beverages than from foods. The median daily TEI of toddlers was 762 kcal, including 272 kcal from beverages (36.4%, r = 0.534) and 492 kcal from foods (63.6%, r = 0.894). Among these, the median daily energy intake from MMDs was 260 kcal, making up 94.6% of the energy intake from beverages (r = 0.959). As the pioneer survey on TWI of toddlers in China based on nationally representative data, attention to the quality and quantity of water intake and actions to better guide parents by both individuals and authorities are eagerly anticipated. Additionally, the revision of the reference value of TWI for Chinese toddlers is urgently required. Full article

The Ogallala aquifer, underlying eight states from South Dakota to Texas, is practically non-recharging south of Nebraska, and groundwater withdrawals for irrigation have lowered the aquifer in western Kansas. Subsequent well-yield declines encourage deficit irrigation, greater reliance on precipitation, and producing profitable drought-tolerant crops like upland cotton (Gossypium hirsutum (L.)). Our objective was to evaluate deficit irrigated cotton growth, yield, and water productivity (CWP) in northwest, west-central, and southwest Kansas in relation to El Niño southern oscillation (ENSO) phase effects on precipitation and growing season cumulative thermal energy (CGDD). Using the GOSSYM crop growth simulator with actual 1961–2000 location weather records partitioned by the ENSO phase, we modeled crop growth, yield, and evapotranspiration (ET) for irrigation capacities of 2.5, 3.75, and 5.0 mmd⁻¹ and periods of 4, 6, and 8 weeks. Regardless of location, the ENSO phase did not influence CGDD, but precipitation and lint yield decreased significantly in southwest Kansas during La Niña compared with the Neutral and El Niño phases. Simulated lint yields, ET, CWP, and leaf area index (LAI) increased with increasing irrigation capacity despite application duration. Southwestern Kansas producers may use ENSO phase information with deficit irrigation to reduce groundwater withdrawals while preserving desirable cotton yields. Full article

In future aviation surveillance, the demand for higher real-time updates for global flights can be met by deploying automatic dependent surveillance–broadcast (ADS-B) receivers on low Earth orbit satellites, capitalizing on their global coverage and terrain-independent capabilities for seamless monitoring. Specific emitter identification (SEI) leverages the distinctive features of ADS-B data. High data collection and annotation costs, along with limited dataset size, can lead to overfitting during training and low model recognition accuracy. Transfer learning, which does not require source and target domain data to share the same distribution, significantly reduces the sensitivity of traditional models to data volume and distribution. It can also address issues related to the incompleteness and inadequacy of communication emitter datasets. This paper proposes a distributed sensor system based on transfer learning to address the specific emitter identification. Firstly, signal fingerprint features are extracted using a bispectrum transform (BST) to train a convolutional neural network (CNN) preliminarily. Decision fusion is employed to tackle the challenges of the distributed system. Subsequently, a transfer learning strategy is employed, incorporating frozen model parameters, maximum mean discrepancy (MMD), and classification error measures to reduce the disparity between the target and source domains. A hyperbolic space module is introduced before the output layer to enhance the expressive capacity and data information extraction. After iterative training, the transfer learning model is obtained. Simulation results confirm that this method enhances model generalization, addresses the issue of slow convergence, and leads to improved training accuracy. Full article

The Brazilian Cerrado biome is known for its high biodiversity, and the role of groundwater recharge and climate regulation. Anthropogenic influence has harmed the biome, emphasizing the need for science to understand its response to climate and reconcile economic exploration with preservation. Our work aimed to evaluate the seasonal and interannual variability of the surface energy balance in a woodland savanna (Cerrado) ecosystem in southeastern Brazil over a period of 19 years, from 2001 to 2019. Using field micrometeorological measurements, we examined the variation in soil moisture and studied its impact on the temporal pattern of energy fluxes to distinguish the effects during rainy years compared to a severe drought spell. The soil moisture measures used two independent instruments, cosmic ray neutron sensor CRNS, and FDR at different depths. The measures were taken at the Pé de Gigante (PEG) site, in a region of well-defined seasonality with the dry season in winter and a hot/humid season in summer. We gap-filled the energy flux measurements with a calibrated biophysical model (SiB2). The long-term averages for air temperature and precipitation were 22.5 °C and 1309 mm/year, respectively. The net radiation (Rn) was 142 W/m², the evapotranspiration (ET) and sensible heat flux (H) were 3.4 mm/d and 52 W/m², respectively. Soil moisture was marked by a pronounced negative anomaly in the 2014 year, which caused an increase in the Bowen ratio and a decrease in Evaporative fraction, that lasted until the following year 2015 during the dry season, despite the severe meteorological drought of 2013/2014 already ending, which was corroborated by the two independent measurements. The results showed the remarkable influence of precipitation and soil moisture on the interannual variability of the energy balance in this Cerrado ecosystem, aiding in understanding how it responds to strong climate disturbances. Full article

Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge cycle data are available. To address these challenges, we propose a capacity degradation estimation method that utilizes shorter charging segments for multiple battery types. A learning-based model called GateCNN-BiLSTM is developed. To improve the accuracy of the basic model in small-sample scenarios, we integrate a single-source domain feature transfer learning framework based on maximum mean difference (MMD) and a multi-source domain framework using the meta-learning MAML algorithm. We validate the proposed algorithm using various LIB cell and battery pack datasets. Comparing the results with other models, we find that the GateCNN-BiLSTM algorithm achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) for cell charging capacity estimation, and can accurately estimate battery capacity degradation based on actual charging data from electric vehicles. Moreover, the proposed method exhibits low dependence on the size of the dataset, improving the accuracy of capacity degradation estimation for multi-type batteries with limited data. Full article

When stroke occurs in pediatric age, it might be mistakenly interpreted as non-accidental head injury (NAHI). In these situations, a multidisciplinary approach is fundamental, including a thorough personal and familial history, along with accurate physical examination and additional investigations. Especially when the clinical picture is uncertain, it is important to remember that certain genetic conditions can cause bleeding inside the brain, which may resemble NAHI. Pediatric strokes occurring around the time of birth can also be an initial sign of undiagnosed genetic disorders. Hence, it is crucial to conduct a thorough evaluation, including genetic testing, when there is a suspicion of NAHI but the symptoms are unclear. In these cases, a characteristic set of symptoms is often observed. This study aims to summarize some of the genetic causes of hemorrhagic stroke in the pediatric population, thus mimicking non-accidental head injury, considering elements that can be useful in characterizing pathologies. A systematic review of genetic disorders that may cause ICH in children was carried out according to the Preferred Reporting Item for Systematic Review (PRISMA) standards. We selected 10 articles regarding the main genetic diseases in stroke; we additionally selected 11 papers concerning patients with pediatric stroke and genetic diseases, or studies outlining the characteristics of stroke in these patients. The disorders we identified were Moyamoya disease (MMD), COL4A1, COL4A2 pathogenic variant, Ehlers–Danlos syndrome (E-D), neurofibromatosis type 1 (Nf1), sickle cell disease (SCD), cerebral cavernous malformations (CCM), hereditary hemorrhagic telangiectasia (HHT) and Marfan syndrome. In conclusion, this paper provides a comprehensive overview of the genetic disorders that could be tested in children when there is a suspicion of NAHI but an unclear picture. Full article

Empirical evidence supports the simultaneous relationship between parental warmth and child-to-parent violence (CPV). However, no studies analyze the lagged effects of perceived parental warmth and the potential impact of cognitive mechanisms legitimizing immoral behavior on this relationship. This study aimed to examine the mediating role of moral disengagement strategies (reconstruction of immoral behavior, obscuring personal responsibility, misrepresenting injurious consequences, and blaming the victim) in the relationship between the perceived paternal and maternal warmth dimensions (warmth-communication and criticism-rejection) during childhood and CPV towards the father and mother. The sample included 2122 Spanish adolescents (57.7% female) aged 13 to 18 years. The Child-to-Parent Violence Questionnaire (CPV-Q), the Mechanisms of Moral Disengagement Scale (MMDS-S), and the Warmth Scale were used as assessment instruments. The results indicate that paternal and maternal warmth-communication is negatively associated with CPV, whereas paternal and maternal criticism-rejection and moral disengagement strategies are positively related to CPV. The mediation models show that the reconstruction of immoral behavior plays a crucial mediation role in the relationship between paternal and maternal warmth-communication and CPV as well as in the relationship between maternal criticism-rejection and CPV. The results emphasize the necessity of early prevention programs for parents promoting positive parenting practices, including parental warmth, to foster children’s adaptive socio-cognitive development. In addition, addressing moral disengagement in adolescents could help prevent or stop a pattern of violent behavior toward parents. Full article