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Abstract Due to the excellent mechanical, chemical, and electrical properties of third-generation semiconductor silicon carbide (SiC), pressure sensors utilizing this material might be able to operate in extreme environments with temperatures exceeding 300 °C. However, the significant output drift at elevated temperatures challenges the precision and stability of measurements. Real-time in situ temperature monitoring of the pressure sensor chip is highly important for the accurate compensation of the pressure sensor. In this study, we fabricate platinum (Pt) thin-film resistance temperature detectors (RTDs) on a SiC substrate by incorporating aluminum oxide (Al2O3) as the transition layer and utilizing aluminum nitride (AlN) grooves for alignment through microfabrication techniques. The composite layers strongly adhere to the substrate at temperatures reaching 950 °C, and the interface of the Al2O3/Pt bilayer remains stable at elevated temperatures of approximately 950 °C. This stability contributes to the excellent high-temperature electrical performance of the Pt RTD, enabling it to endure temperatures exceeding 850 °C with good linearity. These characteristics establish a basis for the future integration of Pt RTD in SiC pressure sensors. Furthermore, tests and analyses are conducted on the interfacial diffusion, surface morphological, microstructural, and electrical properties of the Pt films at various annealing temperatures. It can be inferred that the tensile stress and self-diffusion of Pt films lead to the formation of hillocks, ultimately reducing the electrical performance of the Pt thin-film RTD. To increase the upper temperature threshold, steps should be taken to prevent the agglomeration of Pt films.

Abstract Background Emotional labor is an essential component of nursing practice and is important for Generation Z nursing students born from the mid-1990s to early 2010s. They will become the backbone of the nursing workforce but present more emotional regulation problems. Studies on emotional labor are limited to clinical nurses and influencing factors at the individual level. The impacts of external systems on emotional labor of nursing students have not been explored. This study aimed to quantify the relationship between early clinical exposure and emotional labor and test the moderating effect of family structure on the relationship. Methods The cross-sectional study recruited 467 nursing students using convenience sampling from seven colleges and universities in mainland China. An e-survey created on WJX.CN was used to collect data in January 2023. Emotional labor (surface acting and deep acting) was measured with the Emotional Labor scale. Early clinical exposure (exposure or not and times of exposure) and family structure (nuclear family, extended family, and single-parent family) were assessed with self-reported questions. Descriptive statistics and the linear mixed-effects modeling were used to do the analyses. Results The mean scores of surface acting and deep acting were 26.66 ± 5.66 and 13.90 ± 2.40, respectively. A significant difference in scores of surface acting was not observed for exposure or not, whereas such a significant difference was found for times of exposure. Nursing students from extended families demonstrated significantly lower scores on surface acting while exposed to clinical practice compared with those from nuclear families. Family structure moderated the relationship between times of exposure and surface acting of nursing students when exposed to clinical practice for one time, but the significance disappeared when the times of exposure increased. No significant findings of early clinical exposure on deep acting were observed. Conclusions Early clinical exposure influenced emotional labor, and students from extended families were more likely to get benefits from early clinical exposure. Studies are needed to help students from nuclear families get comparable benefits on emotional labor as those from extended families, and improve deep acting by early clinical exposure.

Abstract Background Hemoglobin-to-red blood cell distribution width ratio (HRR) had great predictive value for the prognosis of malignant tumors and cardiovascular disease. However, its predictive value for the occurrence of acute kidney injury (AKI) in elderly intertrochanteric fracture patients remains unclear. This study aims to analyze the correlation between the early postoperative HRR and the risk of postoperative AKI in elderly intertrochanteric fracture patients. Methods We reviewed the medical records of 307 elderly intertrochanteric fracture patients in this single-center retrospective cohort study. We performed univariate analysis on the relevant parameters, and parameters with significant differences were included in the following logistic regression model for multivariate analysis. Then, we used a receiver operating characteristic (ROC) curve to evaluate the predictive value of the early postoperative HRR level for AKI in elderly intertrochanteric fracture patients. Patients were divided into a high HRR group and a low HRR group according to the cutoff point determined by ROC curve analysis. Subsequently, the relevance between postoperative HRR and AKI was further determined using propensity score matching (PSM) and inverse probability of treatment weighting (IPTW). Results The area under the curve of the early postoperative HRR for predicting postoperative AKI was 0.714 (95% CI: 0.618–0.809). The cutoff value was 5.44. The sensitivity was 72.7%, and the specificity was 70.8%. Patients were divided into low HRR (⩽ 5.44) and high HRR (> 5.44) groups according to the cutoff value. PSM and IPTW analysis indicated that the risk of AKI in the low HRR group was significantly higher than that in the high HRR group in both the matched cohort (OR = 6.914, 95% CI: 1.714–46.603, p = 0.016) and the weighted group (OR = 2.784, 95% CI: 1.415–5.811, p = 0.040). Conclusions Early postoperative HRR is an accurate, accessible, and economical blood test parameter that can predict the risk of postoperative AKI in elderly patients with femoral intertrochanteric fracture.

Abstract Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.

The continuous growth of renewable energy and the load level has posed increasingly severe operational risks to distribution systems. In view of this, this paper combines state estimation with risk assessment, and uses the results of distribution system state estimation based on Unscented Kalman Filter as the input of risk assessment. With the combination, the sampling based on probability distributions in traditional risk assessment methods is no longer needed, thus avoiding the difficulty of updating probability distributions timely according to the latest information in real-time operation. By applying the proposed risk assessment method, the real-time assessment of operational risks in perspectives of bus voltage, branch power, and renewable energy utilization is achieved. Meanwhile, the weight of each risk index is properly determined according to both subjective and objective knowledge by using Analytic Hierarchy Process method and entropy weight method. Case studies show that the proposed method achieves effective assessment of comprehensive risks in the operation of distribution system.

BackgroundChina is one of the main epidemic areas of scrub typhus, and Zhejiang Province, which is located in the coastal area of southeastern China, is considered a key region of scrub typhus. However, there may be significant bias in the number of reported cases of scrub typhus, to the extent that its epidemiological patterns are not clearly understood. The purpose of this study was to estimate the possible incidence of scrub typhus and to identify the main driving components affecting the occurrence of scrub typhus at the county level.MethodsData on patients with scrub typhus diagnosed at medical institutions between January 2016 and December 2023 were collected from the China Disease Control and Prevention Information System (CDCPIS). The kriging interpolation method was used to estimate the possible incidence of scrub typhus. Additionally, a multivariate time series model was applied to identify the main driving components affecting the occurrence of scrub typhus in different regions.ResultsFrom January 2016 to September 2023, 2,678 cases of scrub typhus were reported in Zhejiang Province, including 1 case of reported death, with an overall case fatality rate of 0.04%. The seasonal characteristics of scrub typhus in Zhejiang Province followed an annual single peak model, and the months of peak onset in different cities were different. The estimated area with case occurrence was relatively wider. There were 41 counties in Zhejiang Province with an annual reported case count of less than 1, while from the estimated annual incidence, the number of counties with less than 1 case decreased to 21. The average annual number of cases in most regions fluctuated between 0 and 15. The numbers of cases in the central urban area of Hangzhou city, Jiaxin city and Huzhou city did not exceed 5. The estimated random effect variance parameters σλ2, σϕ2, and σν2 were 0.48, 1.03 and 3.48, respectively. The endemic component values of the top 10 counties were Shuichang, Cangnan, Chun’an, Xinchang, Pingyang, Xianju, Longquan, Dongyang, Yueqing and Qingyuan. The spatiotemporal component values of the top 10 counties were Pujiang, Anji, Pan’an, Dongyang, Jinyun, Ninghai, Yongjia, Xiaoshan, Yinwu and Shengzhou. The autoregressive component values of the top 10 counties were Lin’an, Cangnan, Chun’an, Yiwu, Pujiang, Longquan, Xinchang, Luqiao, Sanmen and Fuyang.ConclusionThe estimated incidence was higher than the current reported number of cases, and the possible impact area of the epidemic was also wider than the areas with reported cases. The main driving factors of the scrub typhus epidemic in Zhejiang included endemic components such as natural factors, but there was significant heterogeneity in the composition of driving factors in different regions. Some regions were driven by spatiotemporal spread across regions, and the time autoregressive effect in individual regions could not be ignored. These results that monitoring of cases, vectors, and pathogens of scrub typhus should be strengthened. Furthermore, each region should take targeted prevention and control measures based on the main driving factors of the local epidemic to improve the accuracy of prevention and control.

BackgroundThe Qingcaosha Reservoir is facing issues of algal blooms and eutrophication, and the resulting increase in the level of chlorination disinfection by-products in the water has been a major concern. ObjectiveTo evaluate the impact of "Algae Monitoring and Control Program in Qingcaosha Reservoir" (hereinafter referred to as the program) on the control of trihalomethanes (THMs) in conventional finished water. MethodsFrom 2011 to 2019, water samples were collected from the Lujiazui Water Plant once per season, one sample each time, and the concentrations of four THMs (trichloromethane, dichlorobromomethane, monochlorodibromomethane, and tribromomethane) were measured in the samples. Using 2014 when the program was implemented as a cut-off point, the entire study period was divided into two phases: pre-implementation (2011–2013) and post-implementation(2014–2019). Segmented linear regression with interrupted time series analysis was applied to assess the concentrations and trends of THMs in the finished water before and after the program launch. ResultsThe concentration of total THMs in finished water increased by 1.561 µg·L−1 (P=0.010) for each season of time extension before launching the program. The change in the concentration of total THMs in finished water was not statistically significant after the program launch, but the THMs concentration showed a decreasing trend as the slope was −0.626 (P=0.001). From 2017 until the end of 2019, the average concentration of THMs in finished water of Lujiazui Water Plant dropped to 10 μg·L−1 or less.ConclusionsThe algae and eutrophication control measures in Qingcaosha Reservoir have achieved good results, controlling THMs in finished water at a low level, and the trend of THMs has changed from a yearly increase pattern before the program to a yearly decrease pattern after the program.

Remote sensing image change detection aims to identify differences between images acquired at different times in the same area, crucial for land management, environmental monitoring, and disaster assessment. Current change detection methods mainly use Siamese or early fusion structures. Siamese networks focus on object features at different times but lack attention to change information, leading to false alarms and missed detections. Early fusion structures focus on fused features but neglect temporal object features, hindering accurate change detection. To address these issues, we propose a novel network, Triplet UNet (T-UNet), employing a triplet encoder to simultaneously extract object features and change features between the pre- and post-time-phase images. To effectively interact features extracted from the three branches of triplet encoder, we propose a multi-branch spatial-spectral cross-attention module. In the decoder, we employ channel attention and spatial attention mechanisms to fully mine and integrate detailed texture and semantic localization information. The proposed T-UNet surpasses seven other state-of-the-art methods on three publicly available datasets. Extensive experiments verify the effectiveness of the proposed structure and modules as well as the superiority of the proposed T-UNet. The source code for the proposed T-UNet is accessible at https://github.com/Pl-2000/T-UNet.

Abstract Fluoropyrimidine-based combination chemotherapy plus targeted therapy is the standard initial treatment for unresectable metastatic colorectal cancer (mCRC), but the prognosis remains poor. This phase 3 trial (ClinicalTrials.gov: NCT03950154) assessed the efficacy and adverse events (AEs) of the combination of PD-1 blockade-activated DC-CIK (PD1-T) cells with XELOX plus bevacizumab as a first-line therapy in patients with mCRC. A total of 202 participants were enrolled and randomly assigned in a 1:1 ratio to receive either first-line XELOX plus bevacizumab (the control group, n = 102) or the same regimen plus autologous PD1-T cell immunotherapy (the immunotherapy group, n = 100) every 21 days for up to 6 cycles, followed by maintenance treatment with capecitabine and bevacizumab. The main endpoint of the trial was progression-free survival (PFS). The median follow-up was 19.5 months. Median PFS was 14.8 months (95% CI, 11.6–18.0) for the immunotherapy group compared with 9.9 months (8.0–11.8) for the control group (hazard ratio [HR], 0.60 [95% CI, 0.40–0.88]; p = 0.009). Median overall survival (OS) was not reached for the immunotherapy group and 25.6 months (95% CI, 18.3–32.8) for the control group (HR, 0.57 [95% CI, 0.33–0.98]; p = 0.043). Grade 3 or higher AEs occurred in 20.0% of patients in the immunotherapy group and 23.5% in the control groups, with no toxicity-associated deaths reported. The addition of PD1-T cells to first-line XELOX plus bevacizumab demonstrates significant clinical improvement of PFS and OS with well tolerability in patients with previously untreated mCRC.

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces. In tunneling practice, the rock mass quality is often assessed via a combination of qualitative and quantitative parameters. However, due to the harsh on-site construction conditions, it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction. In this study, a novel improved Swin Transformer is proposed to detect, segment, and quantify rock mass characteristic parameters such as water leakage, fractures, weak interlayers. The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%, 81%, and 86% for water leakage, fractures, and weak interlayers, respectively. A multi-source rock tunnel face characteristic (RTFC) dataset includes 11 parameters for predicting rock mass quality is established. Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset, a novel tree-augmented naive Bayesian network (BN) is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%. In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset. By utilizing the established BN, a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters, results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Objective: Investigating the effects of MYB proto-oncogene like 2 (MYBL2)-mediated regulation of Cell division cycle associated 8 (CDCA8) expression on the biological activity of cutaneous malignant melanoma cells. Methods: A375 cells with MYBL2 and CDCA8 overexpression and knockdown were evaluated using migration, invasion, and proliferation assays. Besides, cell apoptosis was quantified by flow cytometry. To investigate the tumorigenic effects of MYBL2 knockdown in vivo, A375 cells with MYBL2 knockdown were injected in BALB/C nude mice. Results: The levels of MYBL2 and CDCA8 gene expression were notably elevated in A375 cells in comparison to HaCat cells (P

Vehicle detection is vital for urban planning and traffic management. Optical remote sensing imagery, known for its high resolution and extensive coverage, is ideal for this task. Traditional horizontal bounding box (HBB) annotations often include excessive background, leading to reduced accuracy, whereas oriented bounding box (OBB) annotations are more precise but costly and prone to human error. To address these issues, we propose the Oriented Group R-CNN + Student Model (OGR-SM) framework, a weakly semisupervised oriented vehicle detection method based on single-point annotations. It leverages a small amount of OBB annotations along with a large quantity of single-point annotations for training, achieving performance comparable to fully supervised learning with 100% complete annotations. Specifically, we train the teacher model (OGR) using a small set of accurately annotated OBBs and their corresponding single-point annotations. This model employs an instance-point driven proposal grouping strategy and a group-based proposal assignment strategy enhanced with point location to generate pseudo-OBBs from a large set of weakly annotated single-point data. We then utilize conventional oriented detectors as student models to perform the vehicle detection task in a standard manner. Extensive experiments on two datasets show that our framework, using limited accurate OBBs and many pseudo-OBBs, can achieve or surpass the accuracy of fully supervised models. Our method balances high-quality annotations with data availability, enhancing scalability and robustness for vehicle detection in remote sensing applications.

Abstract Background Lung cancer is a highly prevalent malignancy worldwide and is associated with high mortality rates. While the involvement of endoplasmic reticulum (ER) stress in the development of lung adenocarcinoma (LUAD) has been established, the underlying mechanism remains unclear. Methods In this study, we utilized data from The Cancer Genome Atlas (TCGA) to identify differentially expressed endoplasmic reticulum stress-related genes (ERSRGs) between LUAD and normal tissues. We performed various bioinformatics analyses to investigate the biological functions of these ERSRGs. Using LASSO analysis and multivariate stepwise regression, we constructed a novel prognostic model based on the ERSRGs. We further validated the performance of the model using two independent datasets from the Gene Expression Omnibus (GEO). Additionally, we conducted functional enrichment analysis, immune checkpoint analysis, and immune infiltration analysis and drug sensitivity analysis of LUAD patients to explore the potential biological function of the model. Furthermore, we conducted a battery of experiments to verify the expression of ERSRGs in a real-world cohort. Results We identified 106 ERSRGs associated with LUAD, which allowed us to classify LUAD patients into two subtypes based on gene expression differences. Using six prognostic genes (NUPR1, RHBDD2, VCP, BAK1, EIF2AK3, MBTPS2), we constructed a prognostic model that exhibited excellent predictive performance in the training dataset and was successfully validated in two independent external datasets. The risk score derived from this model emerged as an independent prognostic factor for LUAD. Confirmation of the linkage between this risk model and immune infiltration was affirmed through the utilization of Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The q-PCR results verified significant differences in the expression of prognostic genes between cancer and paracancer tissues. Notably, the protein expression of NUPR1, as determined by immunohistochemistry (IHC), exhibited an opposite pattern compared to the mRNA expression patterns. Conclusion This study establishes a novel prognostic model for LUAD based on six ER stress-related genes, facilitating the prediction of LUAD prognosis. Additionally, NUPR1 was identified as a potential regulator of stress in LUAD.

In order to improve the efficiency of energy utilization, the integrated energy system (IES) has emerged. The IES typically acts as a whole system during operations, the subsystems are separated, and the interests of each system are independent. In this paper, considering the relationship between the various energy systems, non-cooperative game theory is used to establish the optimal dispatch model. The proposed model mainly relies on the relationship between the cooperation and competition among various subsystems to obtain the maximum benefit they can accept. Furthermore, the basic definition is combined with the particle swarm optimization algorithm to solve the problem. The results show that the optimization strategy proposed in this paper can operate safely and reliably, and effectively distribute the benefits of each energy system.

Abstract Ancient pottery, having endured prolonged burial in soil, invariably underwent the process of glaze corrosion. The micromorphology and structural composition of four lead-glazed pottery fragments dating back to the Han Dynasty, excavated in the Qinhan New Town within Xixian New area of Xi’an are elucidated by comprehensive analysis method. The results reveal that the corrosion products predominantly comprised PbCO3, Pb3(PO4)2, CaCO3, and Ca3(PO4)2. Concurrently, the presence of organic substances exhibiting characteristic self-excitation fluorescence in the cracks of the sample is identified through fluorescence microscopy and Fourier infrared spectroscopy. Through a comparative analysis of well-preserved and severely corroded samples, taking into account compositional analysis, observations of corrosion morphology, and the characteristics of corrosion products, it is deduced that the primary corrosion mechanisms involve chemical corrosion, microbial corrosion, and crack corrosion. This study provides a comprehensive depiction of glaze corrosion process inherent to lead-glazed pottery and establishes corresponding corrosion models.

Deep convolutional neural networks have made some progress in skin lesion classification and cancer diagnosis, but there are still some problems to be solved, such as the challenge of small inter-class feature differences and large intra-class feature differences, which might limit the classification performance of the model as high-level and low-level features are not properly utilized. This paper proposes a multi-scale skin cancer image identification network using self-interactive attention pyramid and cross-layer bilinear-trilinear pooling(SPCB-Net), which mainly consists of three proposed sub-modules that are the self-interacting attention pyramid (SAP), the across-layer bilinear-trilinear pooling operation and the global average algorithm(GAA). The SPCB-Net is applied to two representative datasets of medical images in dermatology and histopathology (HAM10000 and NCT-CRC-HE-100K) to demonstrate the effectiveness of in the skin lesion classification. SPCB-Net(ResNet101) achieves 97.10% and 99.87% accuracy on HAM10000 and NCT-CRC-HE-100K respectively, which are both achieved performance improvements of 0.4% compared to the state-of-the-art models. In addition, a large number of experiments on HAM10000 show that the interactive attention pyramid(SPA) proposed in this paper is superior to the common attention module, and the method with a cross-layer bilinear-trilinear pooling is superior to the cross-layer trilinear pooling method. SPCB-Net is configured on Vgg19 and ResNet101 to evaluate the effectiveness of our proposed module. The experimental results show that SPCB-Net has shown state-of-the-art performance in the two field of dermatology and histopathology. Therefore, it is not only well qualified for the task of identifying skin cancer image but also has the potential to identify skin cancer by identifying pathological tissue.

In order to consider the impact of source-load uncertainty on traditional power system planning methods, a medium- and long-term optimization planning method based on source-load uncertainty modeling and time-series production simulation is proposed. First, a new energy output probability model is developed using non-parametric kernel density estimation, and the spatial correlation of the new energy output is described using pair-copula theory to model the uncertainty analysis of the new energy output. Secondly, a large number of source-load scenarios are generated using the Markov chain Monte Carlo simulation method, and the optimal selection method for discrete state numbers is provided, and then the scenario reduction is carried out using the fast forward elimination technology. Finally, the typical time-series curves of the source-load uncertainty characteristics obtained are incorporated into the optimization planning method together with various flexible resources, such as the demand-side response and energy storage, and the rationality of the planning scheme is judged and optimized based on key indicators such as the cost, wind–light abandonment rate, and loss-of-load rate. Based on the above methods, this paper offers an example of the power supply planning scheme for a certain region in the next 30 years, providing effective guidance for the development of new energy in the region.

The decomposition mechanisms of Fe(EDTA)− and Fe(EDTMP)− complexes, widely used in various industrial applications, were investigated through a theoretical approach. Despite their structural similarities, the phosphonic acid and carboxylic acid groups in these complexes lead to vastly different decomposition behaviors. Fe(EDTA)−, stabilized by delocalized π bonds in carboxylic acid groups, exhibited higher stability than that of Fe(EDTMP)−, which has only σ bonds in phosphonic acid groups. Interaction Region Indicator (IRI) analysis revealed that the steric hindrance of Fe(EDTMP)− was stronger than that of Fe(EDTA)−. Ab initio molecular dynamics simulations revealed that Fe(EDTMP)− undergoes rapid decomposition due to the ease of breaking P-C bonds and the repulsion between phosphonic acid groups. In contrast, Fe(EDTA)− decomposes more slowly. These findings suggest the incorporation of phosphonic acid groups for easier degradation pathways when designing organic acid molecules. Understanding these mechanisms provides a basis for developing strategies for wastewater treatment in industrial settings.

Soft magnetic composites (SMCs) play a crucial role in modern electronics and electrical devices, for which challenges remain in maintaining magnetic performance and reducing loss at megahertz (MHz) frequencies. Here, CeFeCo/FeSiAl@ polyurethane (PU) SMCs have been rationally designed with the simultaneous achievement of large relative permeability, high domain-wall resonance frequency, and low power loss. As a rare-earth soft magnetic alloy, the CeFeCo is promising for high-frequency applications due to its easy-plane anisotropy, whereas FeSiAl is advantageous with increased electrical resistivity as well as approaching-zero magnetocrystalline anisotropy and magnetostriction. Hot pressing has been adopted to achieve a dense and homogeneous distribution of CeFeCo and FeSiAl powders in the PU for reduced magnetic dilution and eddy current loss. The resultant SMCs exhibit optional soft magnetic performance with large relative permeability of 21.0, low power loss of 3677.5 kW·m−3 at 5 MHz, and high domain-wall resonance frequency of 386.1 MHz. As such this work provides insights into the design of high-frequency SMCs incorporating novel rare-earth soft magnetic alloys.

Vitamin C (VC) possesses antioxidant and whitening effects. However, its effectiveness is hindered by challenges such as instability, impaired solubility, and limited bioavailability hinder. In this study, VC was encapsulated in nanoliposomes by primary emulsification and high-pressure homogenization. The VC nanoliposomes were comprehensively characterized for their microscopic morphology, particle size, polydispersity index (PDI), and encapsulation efficiency (EE). Orthogonal experiments were designed to optimize the optimal preparation process, and the antioxidant activity, whitening efficacy, transdermal absorption, and stability of VC nanoliposomes were evaluated based on this optimized process. The findings demonstrated the high reproducibility of the optimal process, with particle size, PDI, and EE values of 113.502 ± 4.360 nm, 0.104 ± 0.010, and 56.09 ± 1.01 %, respectively. Differential scanning calorimetry analysis showed effective encapsulation of VC nanoliposomes with better thermal stability than aqueous VC solution. Besides, the VC nanoliposomes demonstrated excellent antioxidant and whitening effects in efficacy experiments, stronger skin permeability in transdermal experiments and fluorescence tracking. Furthermore, storage stability tests indicated that the VC in nanoliposomes remained relatively stable after 60 days of storage. These findings highlighted the potential use of VC nanoliposomes in a wide range of applications for the cosmetic market, especially in the development of ingredients for skin care products.

2,4-dinitrophenol (2,4-DNP) is a new kind of pollutant that is highly toxic and difficult to be biodegraded. In this study, the feasibility of using exogenous growth regulator salicylic acid (SA) to improve the purification ability of Salix matsudana (S. matsudana) seedlings to 2,4-DNP stress was investigated by a hydroponic simulation experiment. The main research results are as follows: (1) After adding exogenous SA, a high concentration of SA (1000 mg·L−1) inhibited the photosynthetic process and the normal physiological process of the chlorophyll fluorescence system of S. matsudana seedlings to a certain extent. When adding a low concentration of SA (10 mg·L−1) to treat S. matsudana seedlings, all exogenous treatment groups could alleviate the stress of 2,4-DNP on the photosynthetic system and chlorophyll fluorescence system of S. matsudana seedlings, and 10 mg·L−1 SA (DNP + S1) was the best. (2) The addition of exogenous SA could alleviate the damage of 2,4-DNP to S. matsudana seedlings by enhancing the activity of its antioxidant enzymes to remove excess reactive oxygen species (ROS) in the body and reducing the level of membrane lipid peroxidation and the size of membrane damage. The treatment with 10 mg·L−1 SA had the best effect. (3) Exogenous low concentration of SA (10 mg·L−1) could alleviate the decline of biomass index of S. matsudana seedlings under 2,4-DNP stress, but a high concentration of SA (1000 mg·L−1) could not alleviate the 2,4-DNP toxicity of S. matsudana seedlings leaves. Exogenous SA could effectively alleviate the growth damage caused by 2,4-DNP stress on S. matsudana seedlings and increase the tolerance threshold range of S. matsudana seedlings to 2,4-DNP (8.81–33.78 mg·L−1). (4) Exogenous addition of SA could increase the removal percentage of 2,4-DNP in Salix matsudana seedlings. Among them, the removal percentage of Salix matsudana was the highest at 10 mg·L−1 SA, which was 1.46 times (5 days) and 1.19 times (10 days) higher than that of the DNP treatment group, respectively. Overall, when SA reached 10 mg·L−1, the photosynthetic productivity of S. matsudana was the highest, and S. matsudana had the best purification effect on 2,4-DNP in wastewater.

Solid-state ionic thermoelectric generators have emerged as promising solutions for efficient harvesting of low-grade waste heat. However, the main challenge in achieving continuous power supply is the low efficiency of thermoelectric conversion. In this work, substantial achievements have been made in improving the thermoelectric conversion characteristics by introducing redox pairs on the electrode surfaces. This approach takes advantage of the synergistic effect of thermal diffusion and thermoelectric effects to maximize the conversion efficiency. To improve the thermoelectric storage and output power performance, Prussian blue was attached to a carbon woven fabric and used as an electrode. The incorporation of Prussian blue/carbon woven fabric electrodes results in an increase in current density output and an instantaneous power density of 3.7 mW/m2·K2. Furthermore, under a temperature gradient of 10 K, the output energy density for 2 h is 194 J/m2, and the Carnot relative efficiency is as high as 0.12% at a hot side temperature (TH) of 30 °C and a cold side temperature (TC) of 20 °C. Our findings validate the efficacy of integrating thermal diffusion and redox reactions in ionic thermoelectric generators, paving the way for the progress of thermocharged devices and their potential commercial applications.

Abstract Aim This study aimed to determine the psychosocial markers associated with pre‐hospital delay among patients with diabetic foot (DF). Design This study has a cross‐sectional design. Methods The participants completed a questionnaire including pre‐hospital time, demographic characteristics, Social Support Rate Scale, Brief Illness Perception Questionnaire and Type D Personality Scale‐14. Bivariate and multivariate analyses were conducted to explore independent associations with pre‐hospital delay. Results Only 1.8% (3/164) of participants arrived at the hospital for medical care in 24 h of symptom onset. Patients with low utilization of social support (p = 0.029), low negative illness perceptions (p = 0.014) and high levels of negative affectivity (p = 0.009) are likely to arrive late at the clinic. Medical staff should pay attention to identifying diabetic patients' Type D personalities and take actions to improve their social support as well as illness perception, so as to reduce the occurrence of hospital delay. Implications for the Profession and/or Patient Care Psychosocial factors play a vital role in the delay in seeking medical treatment for patients with DF. Medical staff need to improve patients' illness perception as well as self‐management ability through health education. Importantly, key family members provide an emotional and psychological support system for diabetic patients. Therefore, nurses need to work with family members together to give information and psychological support during family visits. Additionally, building and maintaining trust with patients is crucial to encouraging individuals to express their concerns and worries. In this case, nurses may identify patients' negative emotions and conduct timely intervention, so as to achieve favourable outcomes. Patient or Public Contribution This study used a convenience sample of 164 participants with DF recruited from the wound clinic of Northern Jiangsu People's Hospital and Yangzhou Hospital of TCM in China.

PurposeTo investigate the effects of the three-dimension visualization system on dry eye in patients after cataract phacoemulsification.MethodsSeventy-one patients (104 eyes) were enrolled in this study and assigned to the conventional microscopic group (CM group) or three-dimension group (3D group). Ocular Surface Disease Index, The Schirmer I test (SIt), lipid layer thickness (LLT), noninvasive tear breakup time (NIBUT) and other factors associated with dry eye were recorded before and 1 week and 1 month after surgery. The operation time and intraoperative light intensity (ILI) were also recorded.ResultsThe ILI in the 3D group was significantly lower than that in the CM group (33% vs. 60%, p

X80 pipeline steel has played a vital role in oil and gas transportation in recent years. However, hydrogen-related issues frequently lead to pipeline failures during service, resulting in significant losses of properties and lives. Three heat treatment processes (furnace cooling (FC), air cooling (AC), and water cooling (WC)) were carried out to investigate the effect of different microstructures on hydrogen-induced cracking (HIC) susceptibility of X80 pipeline steel. The WC sample demonstrated the highest hydrogen embrittlement index, registering at 21.9%, while the AC and FC samples exhibited progressively lower values of 15.45% and 10.98%, respectively. Under equivalent hydrogen charging durations, crack dimensions with a maximum length exceeding 30 μm in the WC sample generally exceed those in the FC sample and AC sample. The variation is attributed to the difference in microstructures of the samples, predominantly lath bainite (LB) in water-cooled samples, granular bainite (GB) in air-cooled samples, and ferrite/pearlite (F/P) in FC samples. The research results demonstrate that the sensitivity of lath bainite (LB) to HIC is significantly higher than that of pearlite, ferrite, and granular bainite (GB). The presence of a large amount of martensite/austenite (M/A) constituents within bainite results in a multitude of hydrogen trap sites. HIC cracks in bainite generally propagate along the profiles of M/A constituents, showing both intergranular and transgranular cracking modes.

Given the widespread prevalence of viruses, there is an escalating demand for antimicrobial composites. Although the composite of dialdehyde cellulose and silver nanoparticles (DAC@Ag1) exhibits excellent antibacterial properties, its weak mechanical characteristics hinder its practical applicability. To address this limitation, cellulose nanofibers (CNFs) were initially ammoniated to yield N-CNF, which was subsequently incorporated into DAC@Ag1 as an enhancer, forming DAC@Ag1/N-CNF. We systematically investigated the optimal amount of N-CNF and characterized the DAC@Ag1/N-CNF using FT-IR, XPS, and XRD analyses to evaluate its additional properties. Notably, the optimal mass ratio of N-CNF to DAC@Ag1 was found to be 5:5, resulting in a substantial enhancement in mechanical properties, with a 139.8% increase in tensile elongation and a 33.1% increase in strength, reaching 10% and 125.24 MPa, respectively, compared to DAC@Ag1 alone. Furthermore, the inhibition zones against Escherichia coli and Staphylococcus aureus were significantly expanded to 7.9 mm and 15.9 mm, respectively, surpassing those of DAC@Ag1 alone by 154.8% and 467.9%, indicating remarkable improvements in antimicrobial efficacy. Mechanism analysis highlighted synergistic effects from chemical covalent bonding and hydrogen bonding in the DAC@Ag1/N-CNF, enhancing the mechanical and antimicrobial properties significantly. The addition of N-CNF markedly augmented the properties of the composite film, thereby facilitating its broader application in the antimicrobial field.

Deep models may have disappointing performance in real applications due to the domain shifts in data distributions between the source and target domain. Although a few unsupervised domain adaptation methods have been proposed to make the pre-train models effective on target domain datasets, constraints like data privacy, security, and transmission limits restrict access to VHR remote sensing images, making existing unsupervised domain adaptation methods almost ineffective in specific change detection areas. Therefore, we propose a source-free unsupervised domain adaptation change detection structure to complete specific change detection tasks, using only the pre-trained source model and unlabelled target data. The GAN-based source generation component is designed to generate synthetic source data, which, to some extent, reflects the distribution of the source domain. Moreover, these data can be utilised in model knowledge transfer. The model adaptation component facilitates knowledge transfer between models by minimising the differences between deep features, using AAM (Attention Adaptation Module) to extract the difference between high-level features, meanwhile we proposed ISM (Intra-domain Self-supervised Module) to train target model in a self-supervised strategy in order to improve the knowledge adaptation. Our SFDA-CD framework demonstrates superior accuracy over existing unsupervised domain adaptation change detection methods, which has 0.6% cIoU and 1.5% F1 score up in cross-regional tasks and 1.4% cIoU and 1.9% F1 score up in cross-scenario tasks, proving that it can effectively reduce the domain shift between the source and target domains even without access to source data. Additionally, it can facilitate knowledge transfer from the source model to the target model.

The discrete Fourier transform (DFT) is the most commonly used signal processing method in modern digital sensor design for signal study and analysis. It is often implemented in hardware, such as a field programmable gate array (FPGA), using the fast Fourier transform (FFT) algorithm. The frequency resolution (i.e., frequency bin size) is determined by the number of time samples used in the DFT, when the digital sensor’s bandwidth is fixed. One can vary the sensitivity of a radio frequency receiver by changing the number of time samples used in the DFT. As the number of samples increases, the frequency bin width decreases, and the digital receiver sensitivity increases. In some applications, it is useful to compute an ensemble of FFT lengths; e.g., 2P−j for j=0, 1, 2, …, J, where j is defined as the spectrum level with frequency resolution 2j·Δf. Here Δf is the frequency resolution at j=0. However, calculating all of these spectra one by one using the conventional FFT method would be prohibitively time-consuming, even on a modern FPGA. This is especially true for large values of P; e.g., P≥20. The goal of this communication is to introduce a new method that can produce multi-resolution spectrum lines corresponding to sample lengths 2P−j for all J+1 levels, concurrently, while one long 2P-length FFT is being calculated. That is, the lower resolution spectra are generated naturally as by-products during the computation of the 2P-length FFT, so there is no need to perform additional calculations in order to obtain them.

This study used data from the National Longitudinal Transition Study 2012 (NLTS 2012) to explore the future goal aspirations of students with extensive support needs who participate in alternate assessments, compared to other students with extensive support needs and to students with other disabilities. We examined students' individualized education program (IEP)/transition planning meeting experiences and postschool goals in relation to their functional, communication, and self-advocacy skills, as well as their school/community support. Students with other disabilities held higher expectations than all students with extensive support needs for future participation in postsecondary education, employment, independent living, and financial independence. All students had higher postschool goal expectations than their parents. Implications for supporting students with extensive support needs and directions for future research and practice are discussed.

Alcohol use ranks as one of the most prevalent health-risk behaviors among Taiwanese adolescents. Possible selves--personalized future-oriented cognitions about the self--are significant motivators of one's actions, which may potentially influence adolescent drinking behavior. This study aimed to estimate the content domain-specific possible selves and their associations with drinking behaviors among Taiwanese adolescents. A total of 225 Taiwanese seventh and eighth graders from a public junior high school were recruited. An anonymous self-reported questionnaire was used to collect data during two time-points at six-month intervals. Results showed that having a "physical appearance" related hoped-for possible self and a "friendship" related feared possible self was associated with adolescent alcohol use after six months. Whereas, having the "physical appearance" related hoped-for and feared possible selves were associated with alcohol problems, at both, baseline and six months later. Future studies could clarify the meaning behind "physical appearance" related possible selves.

Separate school placements persist for students with extensive support needs (ESN) despite longstanding federal mandates for all students with disabilities to be educated in the least restrictive environment. This study extends research on separate school placement for students with ESN to explore the potential impact of locale and charter school policy by determining (1) the percentage of separate schools in states across locales, (2) whether there is a relationship between the percentages of separate schools in each locale and separate school placement rates, and (3) how the number and location of separate special education charter schools change by state over time. Results showed varying proportions of separate schools among each locale in states with different placement rates, a significant moderate difference between the percentage of separate schools in suburban locales and overall separate school placement rate, and few separate special education charter schools. Implications for research and practice are highlighted.

The Individuals with Disabilities Education Act emphasizes that students and parents are equal partners in the individualized education program (IEP) process, including planning for transition to postsecondary education, employment, and community living. This is especially important for English learners (ELs) with disabilities, yet little is known about the transition planning meeting experiences of parents of ELs with disabilities. We examined the transition planning experiences of a nationally representative sample of parents of ELs with disabilities and parents of non-ELs with disabilities through an analysis of data from the National Longitudinal Transition Study 2012 (NLTS 2012). Results showed parents of ELs with disabilities were more likely to perceive their children taking a passive role in the IEP transition planning meeting than parents of non-ELs with disabilities. Differences were found in several predictors of parent experiences in IEP transition planning. Recommendations are suggested for transition research and practice for ELs with disabilities.

The purpose of this study was to examine factors associated with expectations regarding postsecondary education of students with autism spectrum disorder between the ages of 16 and 21 years. We conducted logistic regressions using variables at the student, family, and school levels using the National Longitudinal Transition Study 2012. Logistic regression results showed that parental expectations for attending college was the only significant predictor of students' own educational expectations. Students' adaptive functioning, autonomy levels, demographic and family variables, parent involvement, and prior roles in Individualized Education Program/transition planning were not significant predictors. Future research needs to examine how schools can develop effective ways to increase both students' and parents' expectations toward postsecondary education.

Using National Longitudinal Transition Study 2012 data, this study explored parent and youth expectations in the areas of postsecondary education, employment, independent living, and financial independence. Compared to youth with other disabilities, youth with intellectual and developmental disabilities and their parents had much lower expectations for the four postschool goals, and parent expectations were much lower than youth's own expectations. Also, youth's race, along with their daily living skills and functional abilities, were positively associated with parent and youth expectations in several future goal areas. Our discussion highlights implications for improving the transition experiences of youth with intellectual and developmental disabilities.