Search Results (2,834)

This study introduces two compact wideband substrate-integrated waveguide (SIW) filters fabricated using thin-film technology. The wideband bandpass response is achieved by incorporating interdigital capacitor (IDC) structures into a half-mode SIW (HMSIW) transmission line. An equivalent LC circuit model is formulated to analyze the influence of IDC parameters on the generation of transmission zeros. For the first filter (BPF 1), a third-order IDC coupling configuration is employed, resulting in a 1 dB passband spanning 11 GHz to 18 GHz, a minimum insertion loss of 0.66 dB, three transmission zeros that enhance stopband performance, and a compact core dimension of $0.49{\lambda }_g\times 0.29{\lambda }_g$. For further miniaturization, a modified HMSIW transmission line incorporating a metal-insulator-metal (MIM) capacitor at the equivalent magnetic wall is proposed. This design effectively reduces the transverse dimension of the waveguide while maintaining the original cutoff frequency. Utilizing this configuration, the second bandpass filter (BPF 2) was designed and fabricated employing double-layer ceramic thin-film technology. The resulting filter exhibits a 1 dB passband spanning 10 GHz to 18 GHz, a compact footprint measuring $0.44{\lambda }_g\times 0.23{\lambda }_g$, a minimum insertion loss of 0.58 dB, and features three transmission zeros. The fabricated and measured results of both filters show good agreement with simulations. Compared with previously reported wideband SIW filters, the proposed designs demonstrate comprehensive advantages in fractional bandwidth, insertion loss, out-of-band suppression, and circuit size, providing effective filtering solutions for high-density integration of microwave and millimeter-wave RF systems. Full article

Eggshell thickness is a critical indicator for evaluating egg breakage resistance and hatchability, yet traditional measurement methods remain destructive and inefficient. To address this, this study proposes a robust prediction approach by integrating Gradient Boosting Decision Tree (GBDT) feature optimization with an improved CatBoost algorithm. First, a joint strategy of Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) was employed to eliminate spectral scattering noise and enhance organic matrix fingerprint information. Subsequently, GBDT was introduced for nonlinear feature evaluation to adaptively screen the top 50 wavelengths, effectively mitigating the “curse of dimensionality” and multicollinearity in full-spectrum data. A CatBoost regression model was then constructed using an Ordered Boosting mechanism, supported by a dual anti-overfitting strategy that merged 10-fold nested cross-validation with Bootstrap resampling. Experimental results demonstrate that this method significantly outperforms traditional algorithms in both prediction accuracy and generalization. The coefficients of determination (R²) for the calibration and prediction sets reached 0.930 and 0.918, respectively, with a root mean square error of prediction (RMSEP) of 0.008 mm. Residual analysis confirms that prediction errors follow a zero-mean Gaussian distribution, indicating that systematic bias was effectively eliminated. This research provides a reliable theoretical foundation and technical support for the intelligent grading of poultry egg quality. Full article

Polymer-modified bituminous binders are widely used in road construction due to their enhanced mechanical performance; however, the effectiveness of these materials critically depends on the actual concentration of polymer modifiers, particularly styrene-butadiene-styrene (SBS). This study aims to develop and validate a rapid, reproducible Fourier Transform Infrared Spectroscopy—Attenuated Total Reflectance (FTIR-ATR) spectroscopy method for the quantitative determination of SBS content in polymer-modified bitumen (PMB). Since, to date, there is no clearly defined method for controlling the quantitative content of polymers in PMB, this creates difficulties in accepting the roadway into operation. Calibration PMB samples containing 1–4% SBS were prepared, tested for physical and mechanical properties, and analyzed spectroscopically to identify characteristic absorption bands at 966 cm⁻¹ and 699–760 cm⁻¹. A first-order calibration model was constructed to relate peak intensity to polymer concentration. The results demonstrate a clear linear correlation between SBS content and IR absorption features, confirming the suitability of FTIR as an instrumental method for routine laboratory control. Application of the model allowed determination of actual polymer mass fraction with high accuracy and reproducibility. The findings also showed that increased SBS levels improve softening point, elasticity, and low-temperature resistance, with 3–4% representing a performance-optimal range. Overall, the proposed FTIR-based approach provides an objective and efficient tool for quality control of polymer-modified binders and supports broader standardization efforts in the field. Full article

With the changing demands of society, gears, as fundamental components of mechanical devices, are evolving towards higher reliability and longer service life. To address the issue of thermal scuffing at the gear meshing interface, we propose the introduction of micro/nano-textures to improve the thermal elastohydrodynamic lubrication characteristics of the meshing surfaces, thereby enhancing the lubrication performance and anti-scuffing load capacity of the gear surfaces. First, finite element models with different microstructural features were established. Then, numerical calculations were conducted using computational fluid dynamics (CFD) software to analyze the impact of various micro-texture configurations on the lubrication performance of the tooth surface. Finally, an orthogonal experiment was performed to optimize the groove length, groove width, and areal density of the micro-textures in order to obtain the best processing parameters. The results show that, compared with the triangular, rectangular and trapezoidal micro-textures, the wedge-shaped micro-texture produces the largest pressure difference at the meshing-in and meshing-out points of the texture grooves, which causes the dynamic pressure effect to be more obvious. Compared with the triangular, rectangular and trapezoidal micro-textures, the wedge-shaped micro-texture has the largest bearing capacity and the smallest friction coefficient, so it has better bearing capacity and anti-friction and wear performance. The process parameters were optimized through orthogonal experiments, and the optimal combination of process parameters was obtained as the areal density of 50%, the depth of micro-pits of 12 µm, and the width of micro-pits of 200 µm. Under these optimal parameters, the pressure difference at the meshing-in and meshing-out points of the wedge micro-texture increased significantly by 255.6% compared to the initial model, and the oil film friction coefficient decreased by 17.857% relative to the initial model. These results demonstrate that the micro-texture with optimal parameters significantly enhances the lubrication and anti-friction/wear performance of the tooth surface. Full article

Background: Guanine-rich DNA regions can fold into G-quadruplex (G4) structures, which are prevalent in telomeres and oncogene promoters, making them attractive targets for anticancer therapeutics. Small molecules capable of selectively stabilizing G4 DNA can disrupt telomerase activity and oncogene expression, offering a promising strategy for cancer intervention. Methods: A rationally designed series of DPPZ–anhydride-conjugated ligands (1 and 2) and their corresponding quaternized derivatives (1-q and 2-q) were synthesized to investigate the combined effects of π-extension, bromine substitution, and cationic modification on DNA recognition. The synthetic strategy relied on the incorporation of a highly planar DPPZ–anhydride scaffold to enhance π-surface area, followed by selective quaternization to introduce permanent positive charge and reinforce electrostatic interactions with the DNA backbone. All compounds were fully characterized by NMR and spectroscopic methods. The DNA-binding properties of the ligands were systematically evaluated toward duplex (ds-DNA) and G-quadruplex (G4-DNA) structures using UV–Vis absorption titration, fluorescence intercalator displacement (FID) assays, and competitive dialysis experiments. Quaternization markedly enhanced intrinsic binding constants and significantly reduced DC₅₀ values, particularly for G4-DNA. While bromine substitution increased overall binding affinity, it did not substantially improve topology selectivity. Among the series, compound 1-q exhibited the most favorable balance between affinity and G4 selectivity. Results: The interaction of the compounds with BSA was quantified using Stern–Volmer quenching constants, which demonstrated a clear trend of enhanced quenching efficiency upon modification. The binding strength followed a descending order of 1-q > 2-q > 1 > 2, highlighting the superior performance of the first series over the second. These findings indicate that the structural features of 1-q facilitate a more robust interaction within the hydrophobic pockets of the protein. Conclusions: Overall, the results demonstrate that strategic π-conjugation combined with electrostatic reinforcement provides an effective approach for the development of topology-selective DNA-binding ligands. Full article

Background: Solitary fibrous tumors are uncommon fibroblastic neoplasms. These tumors are characterized by the recurrent NAB2-STAT6 gene fusion, which is a hallmark of solitary fibrous tumors (SFTs), particularly those arising in the thoracic cavity. While SFTs are mostly found in the abdomen and pleura, they can occur in various locations, including the head and neck region (6% of cases of SFTs). Solitary fibrous tumors of the thyroid (SFTTs) are extremely rare, accounting for only 0.1% of all thyroid tumors. The gold standard imaging modality for thyroid tumors is ultrasonography, even though distinctive characteristics for these types of neoplasms are absent, making pre-operative diagnosis more challenging. Aim: The aim of this study is to perform a systematic literature review and to describe our case by analyzing the main clinical features, histological diagnostic features and treatments of this rare tumor, in order to clarify the behavior and molecular characteristics of SFTTs. Methods: A comprehensive systematic literature review was conducted according to the PRISMA guidelines for SFTTs. We searched the PubMed and EMBASE databases for articles published up to November 2025. The inclusion criteria include confirmed diagnosis of SFTT, while articles describing unrelated neoplasms or articles that were not in English were excluded. A standardized form was used to extract information on the imaging characteristics, histological diagnosis, treatment and outcome. Results: As of 2025, a total of 43 articles were selected, with 61 reported cases of SFTT in the English literature. Pre-operative diagnosis of SFTT is controversial and usually requires immunohistochemical confirmation. In our case, molecular analysis identified, for the first time, a NAB2ex6–STAT6ex17 fusion, contributing to the molecular characterization of this rare tumor. Conclusions: SFTTs are rare and difficult to diagnose; thus, they require a multidisciplinary approach for accurate diagnosis and management. The combination of imaging, cytology, histopathology, and molecular testing is essential in distinguishing SFTTs from other thyroid malignancies. Surgical excision remains the mainstay of treatment, and long-term follow-up is recommended due to the potential risk of recurrence or metastasis. Full article

Nearly 2 million stillbirths occur globally each year. These outcomes are often driven by disparities in healthcare access, especially in low- and middle-income countries, where limited resources and shortages of trained medical personnel further increase preventable risks. Addressing these challenges requires not only strengthening healthcare systems but also enhancing intervention strategies. In this context, the development of decision-support systems becomes essential to dynamically identify at-risk pregnancies and improve fetal outcomes. Therefore, we propose AI-FRS (Artificial Intelligence–Fetal Risk Prediction System), a decision support tool for fetal risk prediction, designed to classify fetal conditions as healthy or at risk, using clinical data from Mexican obstetric patients. AI-FRS is built upon seven distinct machine learning models, systematically evaluated through 127 first-order ensemble combinations using hard voting. To further enhance predictive performance, we assessed 32,752 second-order ensembles, constructed by combining top-performing first-order ensembles across recall, precision, and F1-score metrics. The final selected model, called BSOEM, achieved a robust F1-score of 0.812, providing a more balanced and robust decision-making framework than individual models or simple ensembles. Additionally, we conducted an interpretability analysis to identify the clinical variables with the greatest contribution to model predictions, strengthening the system’s transparency and potential clinical trust. AI-FRS features a user-friendly interface specifically designed to facilitate adoption by healthcare professionals. This provides a fast and clinically applicable AI tool for intrapartum and peripartum risk detection in obstetrics, supporting clinical decision-making and improving fetal health outcomes. Full article

A spatially resolved investigation of bacterial inactivation using a radiofrequency (13.56 MHz) capacitively coupled plasma (RF CCP) discharge operating in ambient air at 4.0 mbar is presented. The plasma was generated in a parallel-plate reactor without external gas precursors and characterized using Langmuir probe diagnostics and optical emission spectroscopy (OES). Electron densities on the order of 109 cm³ were measured near the powered electrode, exhibiting pronounced axial and radial gradients across the discharge volume. OES revealed strong excitation of oxygen- and nitrogen-containing emitters, including O I (777 nm), N₂ s positive system (337–380 nm), and N₂⁺ first negative system features, with emission intensities increasing monotonically with applied RF power. The bactericidal performance was evaluated using Escherichia coli American Type Culture Collection (ATCC) 11775 exposed at different axial and radial positions within the reactor. At a fixed exposure time of 60 s, the log₁₀ reduction increased nonlinearly with RF power, rising from 0.29 at 20 W to 0.81 at 40 W, followed by a sharp transition to the assay reporting ceiling (≥2.95-log₁₀ under the adopted half-count correction) at 50 W and above. Time-resolved measurements at 50 W demonstrated rapid inactivation kinetics, with measurable reductions occurring within 5–10 s and reaching the reporting ceiling within 60 s. In contrast, samples positioned at the chamber periphery or approximately 20 cm from the discharge center exhibited negligible inactivation, confirming strong spatial localization of the biocidal effect. These results identify a threshold-like operating regime in which increased discharge intensity produces rapid inactivation in the plasma core while remaining strongly position dependent. The findings establish medium pressure, air-based RF CCP as an efficient, gas-free, and spatially controllable platform for localized surface decontamination under non-thermal conditions. Full article

In industrial robots, harmonic drive flexible bearings are prone to faults, and fault diagnosis is essential for preventing unexpected downtime. However, vibration signals acquired from robot joints are often non-stationary and contaminated by strong multi-source interference, including motion-induced interference and vibrations induced by the deformation of flexible components. Such interference severely masks the subtle signatures of faults. To address this issue, this paper proposes a fault diagnosis framework that leverages multi-channel vibration signals to enhance fault-related features. First, angular resampling is applied to eliminate speed-induced non-stationarity. Second, envelope extraction is utilized to obtain demodulated signals suitable for independent component analysis (ICA). Subsequently, ICA is employed to extract fault-related components from the multi-channel signals. Finally, the fault-related independent component is identified and analyzed via envelope order spectrum analysis. Experimental validation on an industrial robot under both single-joint and multi-joint operating conditions demonstrates the effectiveness of the proposed framework. The method suppresses multi-source interference and achieves accurate fault diagnosis for flexible bearings under complex operating conditions, with quantitative validation confirming the diagnostic performance of the proposed framework. Full article

Radiomics may aid the noninvasive characterization of adrenal incidentalomas; however, reproducibility is limited by methodological heterogeneity. In this retrospective, single-center, exploratory study, we tested whether radiomic features from baseline non-enhanced computed tomography (NECT) discriminate benign from malignant/metastatic adrenal lesions and contextualized results with a PRISMA 2020 systematic review (PubMed/Scopus 2017–2025; PROSPERO CRD420251276627). Thirty-three patients (36 lesions: 12 lipid-rich adenomas, 9 lipid-poor adenomas, 6 pheochromocytomas, 7 malignant/metastatic lesions, 2 myelolipomas) were included; myelolipomas were excluded from primary comparisons. Two abdominal radiologists performed consensus 3D segmentation on NECT. Using LIFEx (v7.8.0) and IBSI definitions, 42 features were extracted and z-score standardized. LASSO selected four heterogeneity descriptors: First-order Entropy, gray-level co-occurrence matrix (GLCM) entropy, gray-level size zone matrix (GLSZM) non-uniformity, and neighboring gray tone difference matrix (NGTDM) busyness. Heterogeneity increased from lipid-rich adenomas to pheochromocytomas and malignant/metastatic lesions (Kruskal–Wallis, all p < 0.001. Pairwise separability, measured using the Vargha–Delaney A index (VDA) as a rank-based measure of separability, was highest for lipid-rich adenomas versus malignant/metastatic lesions (0.93), intermediate for lipid-poor adenomas versus pheochromocytomas (0.73), and lowest for lipid-rich versus lipid-poor adenomas (0.64). The review identified 18 eligible CT radiomics studies that consistently reported higher entropy/non-uniformity in pheochromocytomas and malignant lesions than in lipid-rich adenomas. Global heterogeneity metrics on NECT may complement conventional CT criteria in indeterminate lesions; external validation with robust reference standards is needed in larger, multicenter cohorts with harmonization. Full article

Traditional social recommendation methods often focus on static representations of users and items, neglecting dynamic changes in user interests and item attractiveness over time, which makes it challenging to adapt to temporal variations in user interests. Additionally, the propagation of information along explicit social relationships tends to over-smooth features and weaken individual preferences, while static implicit relationships may increase short-term noise. Thus, a Dual-channel Controllable Diffusion Network based on Hybrid Representations (HR-DCDN) is proposed for social recommendation. The HR-DCDN first incorporates temporal factors by combining dynamic and static representations to capture changes in user interests and item attractiveness. Then, our method proposes a dual-channel aggregation mechanism to obtain higher-order representations of users and items. Explicit social relationships serve as the social-influence channel, while implicit social relationships discovered via dynamic implicit relationship mining constitute the preference-homophily channel. In addition, a learnable polynomial spectral filter incorporates residual connections and dual-channel fusion information at each propagation step, stabilizing deep propagation and alleviating representation homogenization to a limited extent while preserving high-frequency preference information. Finally, we jointly optimize a cross-layer InfoNCE objective on the perturbed interaction branch with the supervised rating loss, which provides an additional empirical regularization effect, improves robustness, and helps preserve representation diversity without altering the graph structure. Experimental results demonstrate that our model outperforms baseline methods on two real-life social datasets. Full article

To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrated solution combining a neighborhood feature aggregation-based graph attention network (NFA-GAT) and multi-agent deep deterministic policy gradient (MADDPG). First, the D-RTPD problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), which effectively captures the stochastic game characteristics of multi-regional agents and the partial observability of grid states. Second, the NFA-GAT is designed to enhance agents’ perception of grid operating states: by introducing a spatial discount factor, it realizes rational aggregation of multi-order neighborhood information while modeling the attenuation of electrical quantity influence with topological distance. Third, a prior-guided mechanism is integrated into the MADDPG framework to eliminate constraint-violating actions by setting their actor logits to negative infinity, improving training efficiency and strategy reliability. Simulation validations on the IEEE 118-bus test system (75.2% RES installed capacity ratio) show that the proposed method achieves efficient training convergence. Compared with the multi-layer perceptron (MLP) structure, it attains higher cumulative reward values and scenario win rates. When compared with traditional model-driven (ADMM) and data-driven (Q-MIX) methods, the proposed method balances solution efficiency, operational safety (98.7% maximum line load rate, zero power flow violation rate), and economic performance ($12,845 daily dispatch cost), providing a reliable technical support for D-RTPD under high-proportion RES integration. Full article

This work presents the first systematic development of the inverse scattering transform for matrix nonlinear Schrödinger equations in the case where the discrete spectrum has triple poles, under nonzero boundary conditions at infinity. These systems arise physically as reductions modeling spinor Bose-Einstein condensates with hyperfine spin $F=1$ and find applications in nonlinear optics. A uniformization variable is employed to map the underlying Riemann surface to the complex plane, enabling a complete characterization of the analyticity, symmetries, and asymptotic behaviors of the Jost functions and scattering data. Extending the established framework for simple and double poles, we show that $\mathrm{rank} P(x,t,z_n)=3$ requires a third-order zero of $\det a\left(z\right)$ at $z=z_n$, while $\mathrm{rank} P(x,t,z_n)=2$ necessitates a fourth-order zero—a nontrivial feature absent in lower-order cases. The discrete spectrum for both rank configurations is fully characterized, and the full singular behavior near a triple pole is derived, respecting the quartet symmetry $z_n$, $z_n^{*}$, $v{k}_0^2/z_n$, $v{k}_0^2/z_n^{*}$ imposed by the nonzero boundary conditions. Solving the resulting matrix Riemann-Hilbert problem with triple poles yields the potential reconstruction formula and, in the reflectionless case, explicit expressions for general N-triple-pole soliton solutions, with a detailed example for $N=1$ presented to illustrate the construction. Full article

Postoperative stroke is a serious and fatal condition that often affects elderly surgical patients. This rare but severe complication arises from complex interactions between comorbidities, physiologic instability and demographic disturbances that traditional risk tools often fail to capture.This study aims to develop and validate a machine learning model with an improved ability to predict the risk of postoperative stroke in elderly patients utilising the comprehensive clinical and demographic ICU data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. External validation was performed on MIMIC-III and the eICU Collaborative Research Database, with eICU being the primary validation set. We identified postoperative surgical intensive care unit (SICU) patients aged 55 years or older from all databases. A strict temporal window of the first 24 h of ICU admission was applied across all three datasets while extracting features like laboratory measurements and vital sign summaries in order to ensure that all predictor values were derived from a fixed observation period at the beginning of ICU stay. After preprocessing, applying Multivariate Imputation by Chained Equations (MICE) imputation and initial screening of 88 candidate variables, 20 clinically meaningful predictors were selected through a multistage feature selection pipeline incorporating RFECV and permutation importance. SHAP analysis and LIME analysis were used for interpretability. We evaluated ten machine learning techniques, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNNs), Support Vector Machine (SVM–RBF Kernel), Gradient Boosting (GBDT), Neural Network, XGBoost, CatBoost, Naive Bayes. Among them, Random Forest demonstrated strong predictive performance by achieving an AUROC of 0.8072 (95% CI [0.7890, 0.8253]) on the internal validation set. The model also achieved AUROC of 0.7557 (95% CI [0.7267, 0.7794]) and 0.9144 (95% CI [0.8893, 0.9378]) on the external validation sets eICU and MIMIC-III, respectively. Mean systolic blood pressure, Elixhauser score, minimum calcium, and minimum INR (PT) were consistently identified as the most influential predictors through both SHAP analysis and LIME analysis, thus strengthening model interpretability. Our findings suggest that a Random Forest-based predictive model can provide an accurate and generalisable prediction of postoperative stroke in elderly ICU patients using routinely collected physiologic and laboratory data. This also supports early risk stratification and targeted postoperative monitoring. Full article

To address inverse-velocity amplification and numerical instability of axisymmetric parallel mechanisms near dead-point regions, this paper proposes a low-dimensional feature representation and stable inverse-solving framework based on Fourier-encoded Plücker line fields. The limb axes are first represented by normalized Plücker line vectors, and the discrete rod-axis set is lifted to a circumferential continuous line field. A compact feature vector composed of first-order Fourier coefficients is then constructed, from which the continuous feature coefficients and the corresponding feature Jacobian are derived in closed form. Under constant-length constraints, feasible sensitivity and worst-case gain are introduced to characterize local inverse amplification, and a weighted damped KKT inverse solver is formulated to obtain globally bounded inverse solutions for feature velocities. Numerical results show that, in the ideal axisymmetric model, higher-order harmonics remain at numerical-residual levels and the first-order truncation stays dominant, while the most unfavorable amplification location is governed by the trough of feasible sensitivity. For fully reachable targets, the proposed solver reduces the peak generalized velocity by about 4.32%. For targets containing unreachable components, the damped KKT inverse introduces only a small additional residual while keeping the velocity bounded. Additional tests under mild geometric perturbations show that non-ideal errors mainly affect low-order fitting accuracy and higher-order spectral leakage, whereas the peak worst-case gain and the peak-shaving ratio remain largely stable. These results demonstrate that the proposed framework provides a unified description for inverse velocity mapping of axisymmetric parallel mechanisms with analytical interpretability, global boundedness, and robustness under mild geometric imperfections. Full article