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An explicit solution, considering the interface bending resistance as described by the Steigmann–Ogden interface model, is derived for the problem of a spherical nano-inhomogeneity (nanoscale void/inclusion) embedded in an infinite linear-elastic matrix under a general uniform far-field-stress (including tensile and shear stresses). The Papkovich-Neuber (P-N) general solutions, which are expressed in terms of spherical harmonics, are used to derive the analytical solution. A superposition technique is used to overcome the mathematical complexity brought on by the assumed interfacial residual stress in the Steigmann-Ogden interface model. Numerical examples show that the stress field, considering the interface bending resistance as with the Steigmann–Ogden interface model, differs significantly from that considering only the interface stretching resistance as with the Gurtin–Murdoch interface model. In addition to the size-dependency, another interesting phenomenon is observed: some stress components are invariant to interface bending stiffness parameters along a certain circle in the inclusion/matrix. Moreover, a characteristic line for the interface bending stiffness parameters is presented, near which the stress concentration becomes quite severe. Finally, the derived analytical solution with the Steigmann–Ogden interface model is provided in the supplemental MATLAB code, which can be easily executed, and used as a benchmark for semi-analytical solutions and numerical solutions in future studies.

In order to study the effects of microstructure and mechanical properties of 20Cr carburized steel high-speed linear guide under different aging treatment conditions, the microstructure, phase composition and residual stress of linear guide before and after aging treatment were analyzed by scanning electron microscopy (SEM), Х-ray diffractometer (XRD) and X-ray stress tester. The results show that after aging treatment, the martensite structure in the surface layer of 20Cr carburized steel is gradually refined, the fine carbides is precipitated and some residual austenite decompose, the surface hardness of the guide rail increases and the maximum hardness is 729.3HV1/5. Affected by the transformation of residual austenite structure and the change of ferrite content, the residual stress of the carburized layer of the guide rail is eliminated to varying degrees. After heated to 200 °C for 13 h, the residual stress relief effect on the surface of the guide rail is the most obvious, the residual stress value is reduced to -127.17 MPa and the elimination rate is as high as about 54.05%, and there is a polynomial function relationship between the residual stress and the depth of the carburized layer.

Layered Delafossite-type Lix(M1M2M3M4M5…Mn)O2 materials, a new class of high-entropy oxides, were synthesized by nebulized spray pyrolysis and subsequent high-temperature annealing. Various metal species (M = Ni, Co, Mn, Al, Fe, Zn, Cr, Ti, Zr, Cu) could be incorporated into this structure type, and in most cases, single-phase oxides were obtained. Delafossite structures are well known and the related materials are used in different fields of application, especially in electrochemical energy storage (e.g., LiNixCoyMnzO2 [NCM]). The transfer of the high-entropy concept to this type of materials and the successful structural replication enabled the preparation of novel compounds with unprecedented properties. Here, we report on the characterization of a series of Delafossite-type high-entropy oxides by means of TEM, SEM, XPS, ICP-OES, Mössbauer spectroscopy, XRD including Rietveld refinement analysis, SAED and STEM mapping and discuss about the role of entropy stabilization. Our experimental data indicate the formation of uniform solid-solution structures with some Li/M mixing.

The carbon fiber (T300-6K) with a three-dimensional four-way braiding structure was selected as the reinforcement, and the 3D-Cf/6061Al composites at different braiding angles (10°, 20° and 30°) were prepared by the pressure infiltration method, and the effects of different braiding angles on the mechanical properties of the composites were discussed. It is found that the pressure infiltration avoids the weak bonding force between the carbon fiber and the aluminum matrix to a certain extent, so that a better infiltration effect can be obtained. The bending performance of the composites become worse with the increase of braiding angle. With the increase of braiding angle, the strain force of the composites also increases, and the greater the elongation is, the better the toughness is. When the braiding angle was larger, the transverse component force of the fiber was larger, the unevenness of the fiber fracture surface increases, the pulling phenomenon was obvious.

The burgeoning demand for zirconium, driven by the rapid development of smart devices, low-carbon energy technologies, and other emerging industries, underscores the importance of understanding the dynamics of its global trade network. However, the evolutionary patterns of the international zircon ore trade network and its resilience to disruptions remain unclear. This study constructs the international zircon ore trade network from 2013 to 2022, analyzes its structural evolution at both the network and node levels, and evaluates its robustness in 2022 using five attack strategies: random node removal, random edge removal, edge degradation, targeted removal based on node degree, and targeted removal based on node betweenness centrality. Our findings reveal that: (1) the international zircon ore trade network exhibits a shift in the import market towards Asia, with the export market dominated by Spain, the United States, and Brazil. China plays a crucial role as a bridge connecting various countries, while Japan exerts significant influence within the network; (2) the network is particularly vulnerable to targeted attacks based on node degree and betweenness centrality, highlighting the potential for significant disruption following the removal of key nodes. This study provides valuable insights for ensuring the stable and sustainable supply and consumption of zirconium resources, informing the development of targeted policies for countries and relevant industries. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) can reflect changes in brain activity under different states. The electrical signals of the brain are observed to exhibit varying amplitudes and frequencies. These variations are closely linked to different states of consciousness, influencing the internal and external behaviors, emotions, and learning performance of humans. The assessment of personal level of attention, which refers to the ability to consciously focus on something, can also be facilitated by these signals. Research on brain attention aids in the understanding of the mechanisms underlying human cognition and behavior. Based on the characteristics of EEG signals, this research identifies the most effective method for detecting brain attention by adapting various preprocessing and machine learning techniques. The results of our analysis on a publicly available dataset indicate that KNN with the feature importance feature extraction method performed the best, achieving 99.56% accuracy, 99.67% recall, and 99.44% precision with a rapid training time. [ABSTRACT FROM AUTHOR]

Remote sensing change detection (RSCD), which utilizes dual-temporal images to predict change locations, plays an essential role in long-term Earth observation missions. Although many deep learning based RSCD models perform well, challenges remain in effectively extracting change information between dual-temporal images and fully leveraging interactions between their feature maps. To address these challenges, a constraint- and interaction-based network (CINet) for RSCD is proposed. Firstly, a constraint mechanism is introduced that uses labels to control the backbone of the network during training to enhance the consistency of the unchanged regions and the differences between the changed regions in the extracted dual-temporal images. Secondly, a Cross-Spatial-Channel Attention (CSCA) module is proposed, which realizes the interaction of valid information between dual-temporal feature maps through channels and spatial attention and uses multi-level information for more accurate detection. The verification results show that compared with advanced parallel methods, CINet achieved the highest F1 scores on all six widely used remote sensing benchmark datasets, reaching a maximum of 92.00 (on LEVIR-CD dataset). These results highlight the excellent ability of CINet to detect changes in various practical scenarios, demonstrating the effectiveness and feasibility of the proposed constraint enhancement and CSCA module. [ABSTRACT FROM AUTHOR]

With the growing attention given to wearable electronic devices, flexible sensors, as a fundamental component, are emerging as the focal point. However, achieving long-term stability and precise sensing underwater remain two significant challenges that urgently need to be addressed for these sensors. By employing a fully hydrophobic structure, this work achieves a significant breakthrough in underwater applications. The hydrophobic design imparts the ionogel with exceptional water resistance, strong wet adhesion, and remarkable stability in underwater environments. These remarkable advantages make ionogels stand out as strain sensors. The ionogels exhibit extraordinary signal sensing abilities. Specifically, they can capture subtle physiological activities of the human body with precision and sensitivity, both in air or underwater. Besides, the satisfactory thermosensitivity (−2.02% °C−1), high resolution (0.1 °C) and fast response (14 s) ensure that the ionogel becomes a qualified temperature sensor. By integrating with a wireless Bluetooth transmission system, the real-time body temperature can be monitored using a smart cellphone. This work demonstrates the great potential of ionogels in marine exploitation and wearable health monitoring. [ABSTRACT FROM AUTHOR]

Li-rich layered oxides can potentially provide high capacity, thereby enhancing energy density as cathode materials in Li-ion batteries. However, one of the main drawbacks is their low cycling stability. It has been proposed that the structural stability of a solid solution compound might be enhanced by exploiting the high-entropy concept. Here, we studied two Li-rich layered oxide cathode materials with multiple cations in their transition metal sites, categorized as medium or high entropy: Li(Li0.2Co0.18Ni0.18Mn0.44)O2 and Li(Li0.2Co0.18Ni0.18Mn0.18Ti0.26)O2. The synthesized materials, however, experienced a large capacity loss during the first charge/discharge cycle. We performed first-principles calculations to understand the mechanism behind the capacity fading and discovered significant structural changes in both systems. Specifically, we observed extensive Li/Ni interchange, migration of transition metal ions to Li sites, and formation of secondary phases. For the Ti-containing material, which shows a larger capacity fade than the other system, we even observed the formation of a spinel phase. The computed enthalpies of secondary phase formation reactions exhibit large negative values. However, the estimated (maximum) configurational entropy contributions to the free energies of these reactions are much smaller and therefore not determining factors. This study provides crucial insights into degradation mechanisms in Li-rich high-entropy systems, aiding the future design and development of advanced cathode materials for next-generation lithium-ion batteries. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for enhancing fault diagnosis in industrial equipment systems through the application of sensor network-driven knowledge graph-based in-context learning (KG-ICL). By focusing on the critical role of sensor data in detecting and isolating faults, we construct a domain-specific knowledge graph (DSKG) that encapsulates expert knowledge relevant to industrial equipment. Utilizing a long-length entity similarity (LES) measure, we retrieve relevant information from the DSKG. Our method leverages large language models (LLMs) to conduct causal analysis on textual data related to equipment faults derived from sensor networks, thereby significantly enhancing the accuracy and efficiency of fault diagnosis. This paper details a series of experiments that validate the effectiveness of the KG-ICL method in accurately diagnosing fault causes and locations of industrial equipment systems. By leveraging LLMs and structured knowledge, our approach offers a robust tool for condition monitoring and fault management, thereby improving the reliability and efficiency of operations in industrial sectors. [ABSTRACT FROM AUTHOR]

The Chinese government introduced the green credit policy (GCP) in 2007 to address deteriorating ecological conditions. Using an enterprise-heterogeneity dynamic stochastic general equilibrium (DSGE) model that includes a financial accelerator mechanism, uncertainty shocks, and GCP, we reveal that GCP significantly reduces emissions. However, this effect is inhibited by rising external uncertainty, as evidenced by the findings from impulse response and welfare loss analyses. Additionally, this negative effect and inhibition are more pronounced for privately-owned enterprises (POEs) while muted for state-owned enterprises (SOEs). Moreover, this study presents that uncertainty weakens the role of GCP through two channels: tightening firms' financing constraints and discouraging investment. Building upon the theoretical analysis above, we validate our findings by employing detailed firm-level data and the difference-in-differences (DID) method. The empirical results confirm the robustness of our findings. This study offers theoretical and quantitative support for developing GCP in China. [ABSTRACT FROM AUTHOR]

Tuned mass damper (TMD) is a single‐terminal damper, while inerter is a two‐terminal one, which are effective control devices. So, a hybrid damper with a series‐parallel inerter system and a TMD (SPIS‐TMD) in series is proposed. The main work of the manuscript is as follows. Firstly, based on the mechanical diagram of SPIS‐TMD and its equipment on the roof of a high‐rise structure, the general form of seismic motion equation was derived using dynamic finite element technology. Secondly, based on the method of quadratic decomposition for power spectrum density function (QD‐PSDF), the concise analytic solutions for zero‐, first‐, and second‐order response spectral moments (ZFSO‐RSMs) of the general response of structure with SPIS‐TMD were deduced. Thirdly, in response to the numerous parameters that affect the safety of high‐rise structures and the varying difficulty for obtaining SPIS‐TMD's parameters, an optimization analysis technique was proposed, which is constrained by dynamic reliability and SPIS‐TMD's parameter weights. Finally, three examples were given and results show the following. (1) The proposed analytical solutions for ZFSO‐RSMs are correct and high efficiency and can be extended to analysis of stationary seismic responses of general linear structures. (2) Calculating results of ZFSO‐RSMs of general responses with the number of vibration modes corresponding to a participation weight of 100% is exactly the same as those with all vibration modes, and the calculating time of the case of the participation weight of 100% is less than 1/12 that of the case of all vibration modes. (3) The failure probabilities of the structure with SPIS‐TMD with optimal parameters using the proposed method (with a limit failure probability of 0.1587), the structure only equipped with TMD with the same TMD's parameters as SPIS‐TMD, and the structure without dampers are 0.1570, 0.7060, and 0.9778, respectively. It indicates that under the same conditions, the hybrid damper SPIS‐TMD has a better damping effect than a single TMD. [ABSTRACT FROM AUTHOR]

Radiomics offers a novel strategy for the differential diagnosis, prognosis evaluation, and prediction of treatment responses in breast cancer. Studies have explored radiomic signatures from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM), but the diagnostic accuracy varies widely. To evaluate this performance, we conducted a meta-analysis performing a comprehensive literature search across databases including PubMed, EMBASE, SCOPUS, Web of Science (WOS), Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data, and the Chinese BioMedical Literature Database (CBM) until March 31, 2024. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC) were calculated. Twenty-four eligible studies encompassing 5588 breast cancer patients were included in the meta-analysis. The meta-analysis yielded a pooled sensitivity of 0.81 (95% confidence interval [CI]: 0.77–0.84), specificity of 0.85 (95%CI: 0.81–0.87), PLR of 5.24 (95%CI: 4.32–6.34), NLR of 0.23 (95%CI: 0.19–0.27), DOR of 23.16 (95%CI: 17.20–31.19), and AUC of 0.90 (95%CI: 0.87–0.92), indicating good diagnostic performance. Significant heterogeneity was observed in analyses of sensitivity (I2 = 74.64%) and specificity (I2 = 83.18%). Spearman's correlation coefficient suggested no significant threshold effect (P = 0.538). Meta-regression and subgroup analyses identified several potential heterogeneity sources, including data source, integration of clinical factors and peritumor features, MRI equipment, magnetic field strength, lesion segmentation, and modeling methods. In conclusion, DCE-MRI radiomic models exhibit good diagnostic performance in predicting ALNM and SLNM in breast cancer. This non-invasive and effective tool holds potential for the preoperative diagnosis of lymph node metastasis in breast cancer patients. [ABSTRACT FROM AUTHOR]

Using a quasi‐natural experiment, we investigate whether the opening of high‐speed railways affects mutual fund holdings. Applying the difference‐in‐difference method, we find that mutual fund holdings in remote listed companies increase after the introduction of high‐speed railways in their cities. This effect is primarily observed within the optimal interval of the railway. Opening high‐speed railways also facilitates mutual fund visits to remote listed companies, resulting in increased holdings. [ABSTRACT FROM AUTHOR]

In this work, we demonstrated the epitaxial growth of a gallium nitride (GaN) buffer structure on 200 mm SOI (silicon-on-insulator) substrates. This epitaxial layer is grown using a reversed stepped superlattice buffer (RSSL), which is composed of two superlattice (SL) layers with different Al component ratios stacked in reverse order. The upper layer, with a higher Al component ratio, introduces tensile stress instead of accumulative compressive stress and reduces the in situ curvature of the wafer, thereby achieving a well-controlled wafer bow ≤ ±50 µm for a 3.3 µm thick buffer. Thanks to the compliant SOI substrate, good crystal quality of the grown GaN layers was obtained, and a breakdown voltage of 750 V for a 3.3 µm thick GaN buffer was achieved. The breakdown field strength of the epitaxial GaN buffer layer on the SOI substrate is estimated to be ~2.27 MV/cm, which is higher than the breakdown field strength of the GaN-on-Si epitaxial buffer layer. This RSSL buffer also demonstrated a low buffer dispersion of less than 10%, which is good enough for the further processing of device and circuit fabrication. A D-mode GaN HEMT was fabricated on this RSSL buffer, which showed a good on/off ratio of ~109 and a breakdown voltage of 450 V. [ABSTRACT FROM AUTHOR]