Search

Abstract Objective Normal aging is associated with brain volume change, and brain segmentation can be performed within an acceptable scan time using synthetic magnetic resonance imaging (MRI). This study aimed to investigate the brain volume changes in healthy adult according to age and gender, and provide age‐ and gender‐specific reference values using synthetic MRI. Methods A total of 300 healthy adults (141 males, median age 48; 159 females, median age 50) were underwent synthetic MRI on 3.0 T. Brain parenchymal volume (BPV), gray matter volume (GMV), white matter volume (WMV), myelin volume (MYV), and cerebrospinal fluid volume (CSFV) were calculated using synthetic MRI software. These volumes were normalized by intracranial volume to normalized GMV (nGMV), normalized WMV (nWMV), normalized MYV (nMYV), normalized BPV (nBPV), and normalized CSFV (nCSFV). The normalized brain volumes were plotted against age in both males and females, and a curve fitting model that best explained the age dependence of brain volume was identified. The normalized brain volumes were compared between different age and gender groups. Results The approximate curves of nGMV, nWMV, nCSFV, nBPV, and nMYV were best fitted by quadratic curves. The nBPV decreased monotonously through all ages in both males and females, while the changes of nCSFV showed the opposite trend. The nWMV and nMYV in both males and females increased gradually and then decrease with age. In early adulthood (20s), nWMV and nMYV in males were lower and peaked later than that in females (p

Abstract Background The low absorption of x-rays in lung tissue and the poor resolution of conventional computed tomography (CT) limits its use to detect lung disease. However, x-ray dark-field imaging can sense the scattered x-rays deflected by the structures being imaged. This technique can facilitate the detection of small alveolar lesions that would be difficult to detect with conventional CT. Therefore, it may provide an alternative imaging modality to diagnose lung disease at an early stage. Methods Eight mice were inoculated with lung cancers simultaneously. Each time two mice were scanned using a grating-based dark-field CT on days 4, 8, 12, and 16 after the introduction of the cancer cells. The detectability index was calculated between nodules and healthy parenchyma for both attenuation and dark-field modalities. High-resolution micro-CT and pathological examinations were used to crosscheck and validate our results. Paired t-test was used for comparing the ability of dark-field and attenuation modalities in pulmonary nodule detection. Results The nodules were shown as a signal decrease in the dark-field modality and a signal increase in the attenuation modality. The number of nodules increased from day 8 to day 16, indicating disease progression. The detectability indices of dark-field modality were higher than those of attenuation modality (p = 0.025). Conclusions Compared with the standard attenuation CT, the dark-field CT improved the detection of lung nodules. Relevance statement Dark-field CT has a higher detectability index than conventional attenuation CT in lung nodule detection. This technique could improve the early diagnosis of lung cancer. Key points • Lung cancer progression was observed using x-ray dark-field CT. • Dark-field modality complements with attenuation modality in lung nodule detection. • Dark-field modality showed a detectability index higher than that attenuation in nodule detection. Graphical Abstract

Background: CPT is a pentacyclic monoterpene alkaloid with a wide spectrum of antitumor activity. Its clinical application is restricted due to poor water solubility, instability, and high toxicity. We developed a new kind of multifunctional micelles to improve its solubility, reduce the side effecs, and obtain enhanced antitumor effects. Methods: We constructed HA-CPT nano-self-assembly prodrug micelles, which combined the advantages of pH-sensitivity, redox-sensitivity, and active targeting ability to CD44 receptor-overexpressing cancer cells. To synthesize dual sensitive HA-CPT conjugates, CPT was conjugated with HA by pH-sensitive histidine (His) and redox-sensitive 3,3′-dithiodipropionic acid (DTPA). In vitro, we studied the cellular uptake and antitumor effect for tumor cell lines. In vivo, we explored the bio-distribution and antitumor effects of the micelles in HCT 116 tumor bearing nude mice. Results: The dual-sensitive and active targeting HA-His-ss-CPT micelles was proved to be highly efficient in CPT delivery by the in vitro cellular uptake study. The HA-His-ss-CPT micelles escaped from endosomes of tumor cells within 4 h after cellular uptake due to the proton sponge effect of the conjugating His and then quickly released CPT in the cytosol by glutathione (GSH). In mice, HA-His-ss-CPT micelles displayed efficient tumor accumulation and conspicuous inhibition of tumor growth. Conclusions: The novel, dual-sensitive, active targeting nano-prodrug micelles exhibited high efficiency in drug delivery and cancer therapy. This “all in one” drug delivery system can be realized in an ingenious structure and avoid intricate synthesis. This construction strategy can illume the design of nanocarriers responding to endogenous stimuli in tumors.

Abstract Background Osseous changes of the temporomandibular joint (TMJ) are related to the progression of temporomandibular disorders (TMD), and computed tomography (CT) plays a vital role in disease evaluation. Objective The aims of this study were to evaluate the image quality and diagnostic value of ultra-high-resolution CT (U-HRCT) in TMD compared to cone-beam CT (CBCT). Methods TMD patients who underwent both CBCT and U-HRCT between November 2021 and September 2022 were retrospectively included. Image quality scores were assigned for four osseous structures (the cortical and trabecular bones of the condyle, articular eminence, and glenoid fossa) by two independent observers from Score 1 (unacceptable) to Score 5 (excellent). Diagnostic classification of TMD was categorized as follows: Class A (no evident lesion), Class B (indeterminate condition) and Class C (definitive lesion). Image quality scores and diagnostic classifications were compared between CBCT and U-HRCT. The Cohen’s Kappa test, Wilcoxon signed-rank test, Chi-square test and Fisher’s exact test were conducted for statistical analysis. Results Thirty TMD patients (median age, 30 years; interquartile range, 26–43 years; 25 females) with 60 TMJs were enrolled. Image quality scores were higher for U-HRCT than for CBCT by both observers (all Ps

Abstract Background Previous studies have explored the stress and turnover intention of healthcare workers, but as important backup talents in the healthcare system, resident physicians have received little attention from researchers, especially after experiencing COVID-19. Therefore, this study aims to evaluate the chronic stress and turnover intention of resident physicians after experiencing COVID-19. Methods From June to August 2022, we conducted a questionnaire survey on resident physicians in the Children's Hospital of Hebei Province through the online platform (Wenjuanxing) to evaluate their chronic stress and turnover intention after experiencing COVID-19. For the collected data, we used frequency and percentage to make the statistical description, the Chi-square test to make a univariate analysis on the scores of chronic stress and turnover intention scale, and binary logistic regression analysis to explore the influencing factors of turnover intention. Results Out of 143 respondents, we finally received 127 questionnaires, with a response rate of 88.81%. Among 127 respondents, 80.31% of resident physicians experienced varying degrees of chronic stress (mild: 36.22%, moderate: 35.43%, severe: 8.66%), and 74.80% of resident physicians showed varying degrees of turnover intention (mild: 23.62%, moderate: 37.79%, severe: 13.39%). Moreover, age (OR = 0.772, P = 0.042), identity (OR = 8.648, P = 0.021), and chronic stress levels (mild: OR = 6.938, P = 0.003; moderate: OR = 44.049, P

Abstract Accurate vestibule segmentation for CT images is of great significance for the clinical diagnosis of congenital ear malformations and cochlear implant. However, it is still a challenging task due to extremely small size and irregular shape of vestibule. Here, a vestibule segmentation network for CT images is proposed under the basic encoder‐decoder framework. Firstly, a residual block based on channel attention mechanism, named Res‐CA block, is designed to guide the network to enhance the important features for the segmentation tasks while suppressing the irrelevant ones. And then, a global context‐aware pyramid feature extraction (GCPFE) module is proposed to capture multi‐receptive‐field global context information. Finally, active contour with elastic (ACE) loss function is adopted to guide network learning more detailed information of the boundary. Furthermore, deep supervision (DS) mechanism is employed to locate the boundaries finely, improving the robustness of the network. The experiments are conducted on the self‐established VestibuleDataset and UHRCT‐Dataset, as well as publicly available retinal dataset, namely DRIVE, to comprehensively verify the robustness and generalization capability of the proposed segmentation network. The experimental results show that the proposed network can achieve a superior performance.

The microstructure, corrosion resistance, and phase-transition process of micro-arc oxidation (MAO) coatings prepared on LaFe11.6Si1.4 alloy surfaces in different electrolyte systems were systematically investigated. Research has demonstrated that various electrolyte systems do not alter the main components of the coatings. However, the synergistic action of Na2CO3 and Na2B4O7 more effectively modulated the ionization and chemical reactions of the MAO process and accelerated the formation of α-Al2O3. Moreover, the addition of Na2CO3 and Na2B4O7 improved the micromorphology of the coating, resulting in a uniform coating thickness and good bonding with the LaFe11.6Si1.4 substrate. The dynamic potential polarization analysis was performed in a three-electrode system consisting of a LaFe11.6Si1.4 working electrode, a saturated calomel reference electrode, and a platinum auxiliary electrode. The results showed that the self-corrosion potential of the LaFe11.6Si1.4 alloy without surface treatment was −0.68 V, with a current density of 8.96 × 10−6 A/cm2. In contrast, the presence of a micro-arc electrolytic oxidation coating significantly improved the corrosion resistance of the LaFe11.6Si1.4 substrate, where the minimum corrosion current density was 1.32 × 10−7 A/cm2 and the corrosion potential was −0.50 V. Similarly, after optimizing the MAO electrolyte with Na2CO3 and Na2B4O7, the corrosion resistance of the material further improved. Simultaneously, the effect of the coatings on the order of the phase transition, latent heat, and temperature is negligible. Therefore, micro-arc oxidation technology based on the in situ growth coating of the material surface effectively improves the working life and stability of La(Fe, Si)13 materials in the refrigeration cycle, which is an excellent alternative as a protection technology to promote the practical process of magnetic refrigeration technology.

BackgroundThe mental health of medical students is an issue worthy of attention, especially during COVID-19. Many studies have shown that depression and anxiety are the main problems faced by medical students. To assess the pooled prevalence of depression and anxiety among medical students worldwide, we conducted this meta-analysis.MethodsAccording to PRISMA, we used a computerized strategy to search studies in EMBASE, PubMed, PsycArticles, Web of Science, and China Biology Medicine disc. The pooled prevalence of depression and anxiety was calculated by a random-effects model. Heterogeneity was explored by subgroup analysis. Sensitivity analysis and publication bias were also carried out in this meta-analysis.ResultsOf 1316 studies, 41 studies were selected based on 36608 medical students. The pooled depression prevalence was 37.9% (95% CI: 30.7–45.4%), and pooled anxiety prevalence was 33.7% (95% CI: 26.8–41.1%). The prevalence of depression and anxiety among medical students varied by gender, country, and continent.ConclusionThe data reported that the prevalence of depression and anxiety among medical students during COVID-19 was relatively higher than those of the general population and the healthcare workers. The impact of COVID-19 on medical students and how to protect the mental health of medical students are needed to determine through further research.Systematic Review Registration[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021274015], identifier [CRD42021274015].

Domain adaptation tasks have raised much attention in recent years, especially, the task of cross-domain sentiment classification, and remarkable success has been achieved on specific domains with large amounts of labeled data. However, annotating enough data in each domain is still expensive and time-consuming, which will produce difficulty in the application of domain adaptation. In this paper, we proposed a Capsule network method with Identifying Transferable Knowledge (CITK) as common knowledge for cross-domain sentiment classification. CITK model uses capsule network to encode the intrinsic spatial part-whole relationship constituting domain invariant knowledge, which bridges the knowledge gap between the source and target domains. In addition, we use Bidirectional Encoder Representations from Transformers (BERT) to convert sentences to equal length, which is called pre-training, in order to obtain more complete semantic embedded representation, so that Significant Consistent Polarity (SCP) words can be more accurate. Extensive experiments are conducted to evaluate the effectiveness of the proposed CITK model on a real world data set of four domains. Experimental results demonstrate that CITK can significantly outperform the state-of-the-art methods for the cross-domain sentiment classification task.

Previous work has recognized the importance of using the attention mechanism to obtain the interaction between aspect words and contexts for sentiment analysis. However, for the most attention mechanisms, it is unrigorous to use the average vector of the aspect words to calculate the context attention. Besides, the feature extraction ability of the model is also essential for effective analysis, the combination of CNN and LSTM can enhance the feature extraction ability and semantic expression ability of the model, which is also a popular research trend. This paper introduces an aspect level neural network for sentiment analysis named Feature Enhanced Attention CNN-BiLSTM (FEA-NN). Our method is to extract a higher-level phrase representation sequence from the embedding layer by using CNN, which provides effective support for subsequent coding tasks. In order to improve the quality of context encoding and preserve semantic information, we use BiLSTM to capture both local features of phrases as well as global and temporal sentence semantics. Besides, we add an attention mechanism to model interaction relationships between aspect words and sentences to focus on those keywords of targets to learn more effective context representation. We evaluate the proposed model on three datasets: Restaurant, Laptop, and Twitter. Extensive experiments show that the effectivess of FEA-NN.

According to previous studies, many neuroanatomical alterations have been detected in patients with tinnitus. However, the results of these studies have been inconsistent. The objective of this study was to explore the cortical/subcortical morphological neuroimaging biomarkers that may characterize idiopathic tinnitus using machine learning methods. Forty-six patients with idiopathic tinnitus and fifty-six healthy subjects were included in this study. For each subject, the gray matter volume of 61 brain regions was extracted as an original feature pool. From this feature pool, a hybrid feature selection algorithm combining the F-score and sequential forward floating selection (SFFS) methods was performed to select features. Then, the selected features were used to train a support vector machine (SVM) model. The area under the curve (AUC) and accuracy were used to assess the performance of the classification model. As a result, a combination of 13 cortical/subcortical brain regions was found to have the highest classification accuracy for effectively differentiating patients with tinnitus from healthy subjects. These brain regions include the bilateral hypothalamus, right insula, bilateral superior temporal gyrus, left rostral middle frontal gyrus, bilateral inferior temporal gyrus, right inferior parietal lobule, right transverse temporal gyrus, right middle temporal gyrus, right cingulate gyrus, and left superior frontal gyrus. The accuracy in the training and test datasets was 80.49% and 80.00%, respectively, and the AUC was 0.8586. To the best of our knowledge, this is the first study to elucidate brain morphological changes in patients with tinnitus by applying an SVM classifier. This study provides validated cortical/subcortical morphological neuroimaging biomarkers to differentiate patients with tinnitus from healthy subjects and contributes to the understanding of neuroanatomical alterations in patients with tinnitus.

The positive temperature coefficient (PTC) effect of the semiconductive layers of high-voltage direct current (HVDC) cables is a key factor limiting its usage when the temperature exceeds 70 °C. The conductivity of the ionic conductor increases with the increase in temperature. Based on the characteristics of the ionic conductor, the PTC effect of the composite can be weakened by doping the ionic conductor into the semiconductive materials. Thus, in this paper, the PCT effects of electrical resistivity in perovskite La0.6Sr0.4CoO3 (LSC) particle-dispersed semiconductive composites are discussed based on experimental results from scanning electron microscopy (SEM), transmission electron microscopy (TEM) and a semiconductive resistance test device. Semiconductive composites with different LSC contents of 0.5 wt%, 1 wt%, 3 wt%, and 5 wt% were prepared by hot pressing crosslinking. The results show that the PTC effect is weakened due to the addition of LSC. At the same time, the injection of space charge in the insulating sample is characterized by the pulsed electroacoustic method (PEA) and the thermally stimulated current method (TSC), and the results show that when the content of LSC is 1 wt%, the injection of space charge in the insulating layer can be significantly reduced.

The semi-conductive layer located between the wire core and the insulation layer in high voltage direct current (HVDC) cable plays a vital role in uniform electric field and affecting space charges behaviors. In this work, the research idea of adding ionic conductive particles to semi-conductive materials to improve the conductive network and reduce the energy of the moving charge inside it and to suppress charge injection was proposed. Semi-conductive composites doped with different La0.8Sr0.2MnO3 (LSM) contents were prepared. Resistivity at different temperatures was measured to investigate the positive temperature coefficient (PTC) effect. Pulse electro-acoustic (PEA) method and thermal-stimulation depolarization currents (TSDC) tests of the insulation layers were carried out. From the results, space charge distribution and TSDC currents in the insulation samples were analyzed to evaluate the inhibitory effect on space charge injection. When LSM content is 6 wt. %, the experimental results show that the PTC effect of the specimen and charge injection are both being suppressed significantly. The maximum resistivity of it is decreased by 53.3% and the insulation sample has the smallest charge amount, 1.85 × 10−7 C under 10 kV/mm—decreased by 40%, 3.6 × 10−7 C under 20 kV/mm—decreased by 45%, and 6.42 × 10−7 C under 30 kV/mm—decreased by 26%. When the LSM content reaches 10 wt. %, the suppression effect on the PTC effect and the charge injection are both weakened, owing to the agglomeration of the conductive particles inside the composites which leads to the interface electric field distortion and results in charge injection enhancement.

Imbalanced classification is one of the most important problems of machine learning and data mining, existing in many real datasets. In the past, many basic classifiers such as SVM, KNN, and so on have been used for imbalanced datasets in which the number of one sample is larger than that of another, but the classification effect is not ideal. Some data preprocessing methods have been proposed to reduce the imbalance ratio of data sets and combine with the basic classifiers to get better performance. In order to improve the whole classification accuracy, we propose a novel classifier ensemble framework based on K-means and resampling technique (EKR). First, we divide the data samples in the majority class into several sub-clusters using K-means, k-value is determined by Average Silhouette Coefficient, and then adjust the number of data samples of each sub-cluster to be the same as that of the minority classes through resampling technology, after that each adjusted sub-cluster and the minority class are combined into several balanced subsets, the base classifier is trained on each balanced subset separately, and finally integrated into a strong ensemble classifier. In this paper, the extensive experimental results on 16 imbalanced datasets demonstrate the effectiveness and feasibility of the proposed algorithm in terms of multiple evaluation criteria, and EKR can achieve better performance when compared with several classical imbalanced classification algorithms using different data preprocessing methods.

Finding an appropriate shape for the releasing building is thoroughly relevant given the energy dissipation and safety requirements of a high dam in a sediment-laden river. Thirty-six physical experiments on trajectory energy dissipation were conducted, researching the influence of three overflow shapes (contraction ratios of 0.5, 0.4, and 0.3) with four sediment concentrations (0, 50, 150, and 250 kg/m3) on the discharge, flow regime, and hydrodynamic pressure of a plunge pool slab. The experimental results demonstrated that the flow coefficient gradually decreased as the contraction ratio decreased in a relatively high weir head, regardless of the sediment concentration. The water nappe narrowed and the length of the longitudinal trajectory increased as the outlet shrinkage and sediment concentration decreased. With the increase in sediment concentration, the nappe regime approached stability, and the flow in the plunge pool tended toward small rolling, causing the impact pressure and fluctuating pressure to increase. Changes in overflow shape had little effect on the position of pressure peak, but the value became lower as the ratio diminished. The influence on the hydrodynamic pressure by outlet shrinkage became attenuated while the sediment concentration increased. The fluctuating energy and vortex scale were enhanced due to the increased viscosity with increasing sediment concentrations.

As a core task of natural language processing and information retrieval, automatic text summarization is widely applied in many fields. There are two existing methods for text summarization task at present: abstractive and extractive. On this basis we propose a novel hybrid model of extractive-abstractive to combine BERT (Bidirectional Encoder Representations from Transformers) word embedding with reinforcement learning. Firstly, we convert the human-written abstractive summaries to the ground truth labels. Secondly, we use BERT word embedding as text representation and pre-train two sub-models respectively. Finally, the extraction network and the abstraction network are bridged by reinforcement learning. To verify the performance of the model, we compare it with the current popular automatic text summary model on the CNN/Daily Mail dataset, and use the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics as the evaluation method. Extensive experimental results show that the accuracy of the model is improved obviously.

Text representation learning is an important but challenging issue for various natural language processing tasks. Recently, deep learning-based representation models have achieved great success for sentiment classification. However, these existing models focus on more semantic information rather than sentiment linguistic knowledge, which provides rich sentiment information and plays a key role in sentiment analysis. In this paper, we propose a lexicon-enhanced attention network (LAN) based on text representation to improve the performance of sentiment classification. Specifically, we first propose a lexicon-enhanced attention mechanism by combining the sentiment lexicon with an attention mechanism to incorporate sentiment linguistic knowledge into deep learning methods. Second, we introduce a multi-head attention mechanism in the deep neural network to interactively capture the contextual information from different representation subspaces at different positions. Furthermore, we stack a LAN model to build a hierarchical sentiment classification model for large-scale text. Extensive experiments are conducted to evaluate the effectiveness of the proposed models on four popular real-world sentiment classification datasets at both the sentence level and the document level. The experimental results demonstrate that our proposed models can achieve comparable or better performance than the state-of-the-art methods.

The effect of sediment on the hydraulics of jet energy dissipation is an urgent issue for high dams built on sediment-laden rivers. Accordingly, flume experiments were conducted using a ski-jump type energy dissipator in flows of four sediment concentrations (0 kg/m3, 50 kg/m3, 150 kg/m3, and 250 kg/m3) to determine the effects on discharge, flow regime, and hydrodynamic pressure in a plunge pool. The results demonstrate that the effect of sediment on discharge is constant, regardless of sediment concentration, when compared to fresh water. The width of the nappe decreased with increasing concentrations of sediment. The length of the jet trajectory increased with upstream water head. The time-averaged pressure and fluctuation pressure both exhibited peaks, describing the impact of the jet on the bottom of the plunge pool. The maximum time-averaged pressure and maximum fluctuating pressure both noticeably increased with upstream water head and slightly increased with sediment concentration for a given flow condition. The results also demonstrated that the dominant frequency of fluctuation trends to lower values, and that both the fluctuating energy and vortex scale increase with increasing sediment concentrations due to increased viscosity. These findings can be used to improve energy dissipation in dams on sediment-laden rivers.

Longitudinal studies have shown that brain white matter lesions are strong predictors of the conversion of unilateral optic neuritis to multiple sclerosis (MS) in Caucasian populations. Consequently brain MRI criteria have been developed to improve the prediction of the development of clinically definite multiple sclerosis (CDMS). In Asian populations, optic neuritis may be the first sign of classical or optic-spinal MS. These signs add to the uncertainty regarding brain MRI changes with respect to the course of unilateral optic neuritis. The aim of this study was to examine the association between brain lesion activity and conversion to CDMS in Chinese patients with unilateral optic neuritis. A small prospective cohort study of 40 consecutive Chinese patients who presented with unilateral optic neuritis was conducted. Brain lesion activity was recorded as the incidence of Gd-enhanced lesions and new T2 lesions. Brain lesions on MRI that were characteristic of MS were defined according to the 2010 revisions of the McDonald criteria. The primary endpoint was the development of CDMS. We found that nineteen patients (48%) had brain lesions that were characteristic of MS on the initial scan. One of these patients (3%) had Gd-enhanced brain lesions. A significantly lower percentage of the patients (10%, p

We have previously shown that traumatic brain injury (TBI) can induce an upregulation of nuclear factor kappa B (NF-κB) and proinflammatory cytokines in the gut, which play an important role in the pathogenesis of acute gut mucosal injury mediated by inflammation. In this work, we investigated whether progesterone administration modulated intestinal NF-κB activity and proinflammatory cytokines expression after TBI in male rats. As a result, we found that administration of progesterone following TBI could decrease NF-κB binding activity, NF-κB p65 protein expression, and concentrations of interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α) in the gut. TBI-induced damages of gut structure were ameliorated after progesterone injections. The results of the present study suggest that the therapeutic benefit of post-TBI progesterone injections might be due to its inhibitory effects on intestinal NF-κB activation and proinflammatory cytokines expression.

Co2Sn1−xTxO4 (T = Al, Sc, and Y; 0 ≤ x ≤ 0.2) magnetic spinel oxides have been synthesized through solid-state reaction. The XRD pattern displays a single-phase cubic structure with space group Fd3m. Moreover, magnetic susceptibility results show that substituting trivalent non-magnetic ions improves the para-ferrimagnetic transition temperature, TC. Magnetic susceptibility results of direct current (dc) and alternating current (ac) showed that the T3+ substitution increased the freezing temperature of the spin-glass state, and was also beneficial to the growth of the spin-glass phase. The apparent magnetic exchange bias effect at low temperatures (T < 20 K) was realized in the Co2Sn1−xTxO4 samples using the pinning effect in the spin-glass phase. Due to the magnetic exchange bias effect, the magnetism of the system dramatically increased at low temperatures. Thermal exchange bias results show that the coercivity field (HC) at 10 K increased from 0 for x = 0 samples to ~ 2.1, 1.3, and 2.9 kOe for Al-, Sc-, and Y-doped samples, respectively. It can optimize magnetic materials and architectures, improving performance and practicality in magnetic data storage, magnetic sensors, spintronics, and magnetic nanoparticles. [ABSTRACT FROM AUTHOR]