Search Results (639)

Conventional ultrasound phantoms typically lack anatomical surface geometry and procedural access constraints, limiting the transferability of acquired skills to clinical practice. The objective of the present work was to develop and describe such a platform for ultrasound-guided thyroid nodule puncture training, including its construction and initial ultrasound appearance. We present a modular, body-mimicking ultrasound phantom platform comprising three components: an anatomically shaped epoxy composite chassis cast from a healthy volunteer and covering the cervical and upper thoracic region, interchangeable gelatin-based inserts representing thyroid (including puncture target lesions) and surrounding tissue structures, and an integrated dual-camera optical feedback system for real-time and post-procedural needle trajectory visualization. Two chassis configurations reflecting different chin and shoulder positions allow deliberate modulation of procedural difficulty. Insert composition can be varied to simulate tissues of differing echogenicity and density, including liquid-filled targets. Under appropriate storage and disinfection conditions, inserts remained usable for up to four weeks in qualitative observation. The optical feedback system supports self-directed learning and structured debriefing. In combination with magnet-based ultrasound needle guidance technology, the platform is intended to support a longitudinal, competency-based training concept with quantifiable performance metrics, enabling systematic documentation of individual learning curves. The presented system is designed to more closely replicate the anatomical and procedural complexity of clinical ultrasound-guided interventions than conventional phantoms and represents a flexible simulation platform for interventional ultrasound education. Full article

Background: As a member of the T-box transcription factor family, TBX18 was found to be involved in ESCC progression, while its role in regulating radiotherapy resistance in ESCC remains unclear. This study was designed to investigate the molecular mechanisms underlying the regulation of radioresistance in ESCC by TBX18. Methods: Sphere formation assay, Transwell invasion assay, and wound healing assay were conducted to show the influence of TBX18 on tumor stemness and epithelial–mesenchymal transition (EMT). Western blot, immunofluorescence, chromatin immunoprecipitation-qPCR (ChIP-qPCR) and dual-luciferase reporter assay were preformed to identify regulatory networks. A nude mouse xenograft tumor model was established to assess the regulatory effect of TBX18 and SIX1 on radioresistance of ESCC in vivo. Results: TBX18 expression was positively associated with stemness markers, including CD44, CD271, and SOX2. TBX18 promoted stemness-associated phenotypes, EMT, migration, invasion, and radioresistance in ESCC cells. Mechanistically, TBX18 directly bound to the SIX1 promoter and transcriptionally activated SIX1 expression. In turn, SIX1 enhanced TBX18 protein stability by suppressing ubiquitin–proteasome-mediated degradation, thereby forming a positive feedback loop. Functional rescue experiments demonstrated that the TBX18/SIX1 axis coordinately maintained stemness and EMT phenotypes and attenuated radiotherapy-induced apoptosis. In vivo studies further confirmed that TBX18 knockdown enhanced radiosensitivity, whereas SIX1 overexpression partially reversed this effect. In addition, immunohistochemical analysis revealed that TBX18 and SIX1 were significantly upregulated in ESCC tissues and positively correlated with each other. Full article

We demonstrate a high-power broadband continuous mode-hop-free (MHF) tunable singly resonant optical parametric oscillator (SRO). To obtain broadband continuous MHF operation, a synchronous etalon-angle locking technique and a feedback-optimized temperature controller were developed based on theoretical investigation. At a pump power of 21 W that was eight times the pump threshold, the measured signal was tuned from 1551.9087 nm to 1568.6549 nm, and the corresponding idler was tuned from 3384.3030 nm to 3307.3073 nm simultaneously. A continuous MHF tuning bandwidth of 2.064 THz was achieved at a tuning speed of 4.7 GHz/s. Continuous MHF operation in the whole tuning band was verified by high-resolution absorption spectroscopy of acetylene and methane, and by the continuous sinusoidal transmission through a Fabry–Perot etalon. The measured powers of the signal at 1560 nm and idler at 3346 nm were 4.12 W and 2.26 W with peak-to-peak fluctuations of ±0.42% and ±0.18%, respectively. These results represent, to the best of our knowledge, the widest continuous MHF tuning bandwidth achieved by a temperature-tuned SRO at high pump power, providing a high-power dual-band coherent source for precision spectroscopy. Full article

Severely saline–alkaline land degradation poses a considerable challenge to sustainable agriculture, owing to high salinity, elevated pH, and nutrient deficiency. To address this, a salt-tolerant nitrogen-fixing bacterium (Bacillus sp.) and a microalga (Chlorella pyrenoidosa) were applied—alone, in combination, or with sheep manure—in a pot experiment with six treatments to examine their individual and combined effects on soil amelioration and wheat (Triticum aestivum L. cv. Jinchun 6) growth. We specifically assessed whether the three-component system outperforms single or dual applications. The bacterial–algal co-inoculation (BA) markedly outperformed single inoculations: shoot biomass increased by 117% and soil organic matter (SOM) by 130%, compared with the control. BA also alleviated oxidative stress, as evidenced by reduced malondialdehyde (MDA) content and elevated superoxide dismutase (SOD) and peroxidase (POD) activities. Scanning electron microscopy (SEM) observations confirmed tight bacterial attachment to algal surfaces. Incorporating sheep manure (BAM) further enhanced these benefits, achieving the lowest pH and electrical conductivity (EC), the highest SOM and available-nutrients, and the greatest wheat biomass. 16S rRNA sequencing showed that BAM increased microbial diversity, shifted community structure, and enriched beneficial genera (Sphingomonas, Flavihumibacter, and Fuscovulum) that were positively correlated with soil nutrient availability and plant stress tolerance, while the halophilic genus Halomonas declined. Collectively, the bacteria–algae–manure co-application establishes positive feedback between soil improvement and functional microbiome recruitment, offering a promising strategy for the remediation of severely saline–alkaline soil. Full article

To address junction-temperature fluctuations and thermal-fatigue degradation of IGBT modules in EV traction inverters under CLTC conditions, this study develops a hierarchical active thermal-management framework. A temperature-dependent loss model coupled with a fourth-order Foster thermal network is first established and evaluated against experimentally derived temperature references. The prediction errors are mainly within ±5 °C over approximately 30–145 °C, with a small number of larger deviations during rapid thermal transients. Speed-based feedforward scheduling, single-variable fuzzy feedback, and dual-variable fuzzy control coordinating switching frequency and cooling intensity are then evaluated in simulation. Rainflow counting and the Miner rule show cumulative-damage reductions of 57.09%, 66.70%, and 81.90%, respectively, while the dual-variable strategy increases the model-based equivalent lifetime from 5.32 to 31.99 years. The results demonstrate the benefit of coordinated heat-generation and heat-dissipation control for inverter thermal reliability. Full article

Client selection is a critical mechanism for ensuring robust convergence in Federated Learning (FL) systems, yet it remains vulnerable to Non-IID data distributions and Byzantine attacks. Deep Reinforcement Learning (DRL) has shown promise for automated client selection, yet existing methods suffer from three structural deficiencies: observation ambiguity, where scalar states cannot distinguish malicious updates from benign heterogeneity; reward fragility, whereby attackers exploit unbounded feedback to hijack policy updates; and risk blindness, as risk-neutral agents overlook the inherent variance in client contributions. To address these deficiencies, we propose InfRA-FL, a robust adaptive framework. A mutual-information-based state construction extracts high-utility features, resolving observation ambiguity. A Saturation-Aware Robust Reward (SARR) mechanism applies soft-clipping to bound each client’s influence on the policy gradient, provably neutralizing reward poisoning. URA-PPO, an uncertainty-aware algorithm with a dual-head Critic, optimizes a risk-penalized objective that shifts the agent from risk-neutral to risk-averse decision-making. Experiments on MNIST and CIFAR-10 under 20% Byzantine adversaries show that InfRA-FL outperforms state-of-the-art baselines by 5–10% in accuracy while accelerating convergence, establishing that principled information-theoretic observation and robust reward design suffice to secure RL-driven federated learning against targeted poisoning. Full article

Channel fading and impulse noise in low-voltage station areas severely hinder data acquisition, and effective estimation of dual-mode channel data and state is the critical prerequisite for adaptive transmission. To address the impact of impulse noise on the performance of Orthogonal Frequency Division Multiplexing (OFDM) power line communication systems, this paper proposes a wireless-assisted joint estimation algorithm of channel fading and impulse noise in dual-mode OFDM systems. Firstly, a model of the dual-mode OFDM system combining power line and wireless links is established, and the initial channel estimation is obtained based on Linear Minimum Mean Square Error (LMMSE), the wireless link is used as an aid to provide a relatively reliable initial symbol estimation for the power line link. Then, a two-stage approach for initial impulse noise estimation is proposed: firstly, the possible impulse noise is located based on constructed residuals to form a support set, followed by refinement of the impulse amplitude using Single Measurement Vector Sparse Bayesian Learning (SMV-SBL). Finally, a decision-feedback iterative update is performed to iteratively optimize the symbol detection, channel estimation, and impulse noise estimation processes. Combined with adaptive dual-mode fusion, this approach improves estimation accuracy. Simulation results demonstrate that the proposed algorithm achieves significant improvements in estimation accuracy and bit error rate. Full article

High-penetration renewable energy integration intensifies net-load fluctuations, renewable energy curtailment, and carbon-emission control challenges in power system dispatch. To improve low-carbon operation and source-load-storage coordination, this study proposes a two-layer alternating coordinated dispatch method with response-gap negative-feedback dual incentives. The dispatch-side layer jointly optimizes thermal generation, wind and photovoltaic power, carbon capture and storage, and ladder-type carbon trading, while the load-storage layer coordinates demand response and energy storage through peak-shaving and renewable-accommodation incentives. Unlike static incentives or dynamic schemes driven only by demand indices, the proposed mechanism updates incentive prices according to the deviation between dispatch-side expected response and load-storage-side realized response. The reconstructed net load is then fed back to the dispatch side, forming a closed-loop alternating coordination process. A typical-day case study shows that, compared with Scenario 1, the proposed method reduces the dispatch-side operating cost by 2.89% and net carbon emissions by 20.4%, while increasing the overall renewable energy accommodation rate by approximately 21.5 percentage points. Sensitivity and uncertainty tests further indicate that the main economic, emission-reduction, and renewable-accommodation benefits remain robust under different lower-layer preference weights and moderate demand-response uncertainty. Full article

Unmodeled uncertainties, such as friction and stage-change collision of the hydraulic cylinder, along with system disturbances, exist in the multi-link erecting system and impede high-precision erecting angle tracking. To tackle these challenges, this study develops a novel control framework characterized by asymptotic prescribed performance based on a distributed dual robust integral of the sign of the error (Dual-RISE) for the electro-hydrostatic multi-link erecting system. First, a precise system model is established by integrating complex multi-link kinematics with the pressure-flow dynamics of the two-stage hydraulic cylinder. A prescribed performance function (PPF) and nonlinear error transformation are then introduced to strictly constrain the tracking error within predefined transient and steady-state boundaries. The proposed framework integrates a distributed dual-loop RISE architecture to simultaneously reject matched and unmatched uncertainties, mathematically enforcing semi-global asymptotic convergence of the tracking error to zero without requiring infinite high-gain feedback. Comparative experiments with Dual-RISE and VFPI controllers demonstrate superior tracking accuracy and boundary protection under different erecting conditions. Full article

Single-source remote sensing image (RSI) cannot simultaneously meet high-spatial and high-temporal resolution requirements, failing to provide decision-makers with timely and accurate monitoring data. Spatiotemporal fusion (STF) of multi-source RSIs represents an efficient and convenient means of producing land-cover observations with high-temporal and high-spatial resolutions. However, current STF approaches still suffer from severe prediction distortion under abrupt changes, long-interval temporal variations, and land-cover type transitions, as well as poor robustness against disturbances in prior data. To address these challenges, a temporal-variation-resistant bidirectional convolution-Transformer generative adversarial network (TRB-GAN) for RSI STF, which comprises a temporal-variation-resistant bidirectional convolution-Transformer generator (TRBG) and a multiresolution input convolution-Transformer discriminator (MICTD), is devised to improve the robustness in predicting time-varying information and enhance STF capability. First, the TRBG designs a temporal-variation-resistant bidirectional encoder to capture prior information and arbitrary time-varying local–global features, enhancing prediction robustness and representation capability for time-varying information. Second, the TRBG designs a dual-guided triple-attention fusion decoder (DTAFD), incorporating dual-guided cross convolution-attention fusion and decision attention fusion. DTAFD dynamically calculates correlations among spectral, spatial, and time-varying information to aggregate heterogeneous features and adaptively performs stepwise weighting and integration, effectively mitigating the adverse impacts from heterogeneous imaging mechanisms and significant resolution gaps. Finally, MICTD and deep supervision enable adversarial learning of local–global structures and spectra across resolutions, providing feedback to the TRBG for producing finer images. Ablation and comparative experiments demonstrate the TRB-GAN achieves superior STF performance and stronger robustness to time-varying disturbances for the widely used CIA and LGC datasets. Full article

Negative sample selection is a major source of uncertainty in landslide susceptibility assessment (LSA), because areas without recorded landslides cannot be directly regarded as stable. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) deformation can provide useful constraints for identifying low-deformation candidate areas. However, in steep alpine canyon terrain, low line-of-sight (LOS) deformation may result from unfavorable SAR viewing geometry rather than true slope stability. To address this problem, this study proposes a geometry-aware InSAR feedback purification sampling strategy (GIFPS) for negative sample selection. GIFPS integrates ascending and descending SBAS-InSAR deformation, C-index-based LOS geometric sensitivity, and model feedback to select more reliable negative samples. The method has been evaluated in the Shigatse region of the Qinghai–Tibet Plateau and compared with buffer-controlled sampling (BCS) and dual-orbit low-deformation intersection sampling (DOLIS). Repeated experiments using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) show that GIFPS consistently improves model performance. Compared with BCS and DOLIS, GIFPS increases the mean ROC-AUC by 4.77 percentage points and 3.58 percentage points, respectively, and increases the mean F1-score by 3.75 percentage points and 2.59 percentage points, respectively. The ROC-AUC improvements are statistically significant according to paired Wilcoxon signed-rank tests. Susceptibility zoning, SHAP interpretation, and sample-distribution diagnostics further show that GIFPS improves spatial discrimination mainly through reliability-oriented negative sample selection, rather than by excessively narrowing the conditioning-factor distribution of candidate samples. These results suggest that GIFPS provides an interpretable InSAR-assisted strategy for negative sample selection in LSA in complex alpine canyon areas. Full article

Sparse-view computed tomography (CT) reconstruction aims to recover high-quality CT volumes from a limited number of X-ray projection images, thereby reducing radiation exposure during image acquisition. However, this problem is inherently ill-posed because each projection provides only indirect line-integral supervision, and different attenuation distributions can explain similar sparse measurements. Existing analytic and iterative methods often suffer from streak artifacts and unstable solutions, while supervised learning-based methods require paired training data and may generalize poorly across anatomical regions or acquisition settings. Neural Radiance Field (NeRF)-based methods have recently shown promise by representing the attenuation field as a continuous coordinate-based function optimized directly from projection images. Nevertheless, these methods mainly enforce projection consistency and do not explicitly use volume-domain uncertainty to guide subsequent reconstruction. In this work, we propose EpiC-NeRF, a CT-specific closed-loop framework that actively feeds estimated epistemic uncertainty back into sparse-view reconstruction. EpiC-NeRF adapts evidential uncertainty estimation and aggregation to the X-ray CT line-integral formulation and maintains the resulting spatial uncertainty in a persistent three-dimensional Epistemic Grid Map. The accumulated uncertainty is used by Epistemic-Adaptive Layer Normalization to modulate intermediate features and by dual active sampling to guide ray- and point-level sample allocation. The newly estimated uncertainty then updates the grid map and guides subsequent optimization iterations, forming a unified feedback loop between uncertainty estimation and CT reconstruction. Experiments on four CT volume datasets demonstrate that EpiC-NeRF achieves improved reconstruction fidelity over existing analytic, iterative, and neural implicit reconstruction methods. Full article

Fluidic oscillators (FOs) are self-excited jet-generating devices without moving parts that convert steady fluid supply into oscillatory jets through inherent flow instabilities. Among various FO configurations, dual-feedback fluidic oscillators (DFFOs) have attracted extensive attention due to their simple structure, high reliability, stable oscillation characteristics, and broad applications in active flow control, heat transfer enhancement, and fluid mixing. However, conventional trial-and-error-based optimization methods are limited by strong parameter coupling and trade-offs among multiple performance objectives, such as oscillation frequency, jet deflection angle, and energy efficiency. This review systematically summarizes recent advances in performance enhancement strategies for DFFOs from the perspective of “from geometric control to intelligent design”. The effects of multi-scale geometric regulation, including macroscopic structures, internal microstructures, and manufacturing-related factors, are discussed. Advanced optimization approaches, including active control, novel configurations, inverse design, and data-driven methods, are further reviewed. Particular attention is given to additive manufacturing challenges and DFFO performance under multiphase flow conditions, including erosion, particle deposition, atomization, and mass transfer. Finally, future perspectives are proposed regarding multi-physical coupling, intelligent optimization, and engineering applications. This review provides a comprehensive reference for the cross-scale performance enhancement and intelligent design of DFFOs. Full article

To address the problems of increased bilateral synchronization error, accumulated beam attitude deviation, and degraded operating stability of a dual-motor-driven lifting beam under eccentric loading, this paper proposes a coordinated synchronization–attitude control method based on online eccentric-load estimation and dynamic differential allocation. First, a two-dimensional dynamic model incorporating overall vertical translation and small-angle beam rotation is established. On this basis, the control task is decomposed into trajectory tracking in the common channel and synchronization–attitude regulation in the differential channel. Second, an online equivalent eccentric-load moment estimator is introduced to extract the dominant eccentric-load effect through differential-channel residuals and first-order low-pass filtering. Then, a dynamic differential allocation mechanism jointly driven by the estimated moment and beam attitude is constructed to adaptively adjust the left–right driving-force difference while maintaining the total lifting force. Furthermore, synchronization-error feedback, attitude feedback, and eccentric-load compensation are unified in the differential control law. Finally, comparative simulations and experiments under step and preset eccentric-loading conditions show that, for an additional mass of 10 kg placed 0.45 m from the nominal beam center, the proposed method reduces the experimental tracking RMSE to 4.31 mm, limits the peak tilt angle to 0.64°, and reduces the steady-state synchronization error to 1.48 mm. These results demonstrate improved tracking accuracy, synchronization consistency, attitude stability, and adaptability to persistent eccentric loading. Full article

As electric vehicles become widespread, home EV chargers have emerged as a key interface between household energy use and daily mobility. However, their design optimization remains insufficiently informed by large-scale user feedback. This study develops a review-driven, interpretable machine learning framework to identify design priorities for home EV chargers. Of the 26,763 reviews collected from the JD e-commerce platform, 23,893 were retained after cleaning. BERTopic extracted raw topics, which were consolidated into ten design dimensions through independent coding, inter-coder agreement assessment, and consensus adjudication. A structured large language model protocol then transformed the reviews into evidence-constrained, aspect-level semantic proxy variables representing evaluative direction and intensity. Coding reliability was evaluated against dual-coder annotations, while a matched absence-as-zero specification examined sensitivity to the treatment of unmentioned dimensions. Platform ratings were subsequently introduced as the prediction target, and repeated data partitions and cross-model SHAP comparisons were used to assess partition- and model-level stability. Charging Performance, Operational Stability, Perceived Product Quality, and Operational Convenience and Portability consistently ranked as the most important factors associated with platform-rated satisfaction. In contrast, Installation Friendliness and After-sales Service showed asymmetric attribution patterns characterized by stronger low-value penalties than high-value gains. The framework supports translating online review evidence into product-level design priorities, while emphasizing that SHAP identifies predictive associations rather than causal effects. Full article