Search Results (688)

Background/Objectives: This study aimed to investigate the influence of shade and thickness on the polymerization of SDR^® flow+, a low-viscosity bulk-fill composite, by assessing its degree of conversion (DC). Methods: An in vitro study was conducted using SDR^® flow+ composite resin. Specimens were prepared at two thicknesses (2 mm and 4 mm) and four shades (Universal, A1, A2, A3). Polymerization was performed using a high-intensity LED curing unit. The DC was assessed using Fourier-transform infrared spectroscopy (ATR-FTIR). Results: Both shade and thickness significantly influenced DC. Thicker specimens (4 mm) exhibited reduced polymerization compared to thinner specimens (2 mm). Darker shades, particularly A3, demonstrated the lowest DC values due to their higher chroma, which limits light penetration. In contrast, the Universal shade achieved higher DC values, even at increased depths, likely due to its greater translucency. Conclusions: Shade and thickness play a critical role in the polymerization of bulk-fill composites. Ensuring adequate polymerization is essential for the longevity of pediatric restorations. Further in vivo research is needed to confirm these findings and assess their clinical implications. Full article

Predicting the blow-off (BO) is critical for characterising the operability limits of gas turbine engines. In this study, the applicability of a low-order extinction prediction modelling, which is based on a stochastic variant of the Imperfectly Stirred Reactor (ISR) approach, to predict the lean blow-off (LBO) curve and the extinction conditions in a hydrogen Rich-Quench-Lean (RQL)-like swirl combustor is investigated. The model predicts the blow-off scalar dissipation rate (SDR), which is then extrapolated using Reynolds-Averaged Navier–Stokes (RANS) cold-flow simulations and simple scaling laws, to determine the critical blow-off conditions. It has been found that the sISR modelling framework can predict the BO flow split ratio at different global equivalence ratios, showing a reasonable agreement with the experimental data. This further validates sISR as an efficient low-order modelling flame extinction tool, which can significantly contribute to the development of robust hydrogen RQL combustors by enabling the rapid exploration of combustor operability during the preliminary design phases. Full article

Background: Peripheral endovascular intervention (PEVI) is routinely performed using standard-dose radiation (SDR), which is associated with elevated levels of radiation. No study has evaluated the outcomes of minimal-dose radiation (MDR) in PEVI. Methods: We performed a prospective observational study of 184 patients (65 ± 12 years) at an academic medical center from January 2019 to March 2020 (mean follow-up of 3.9 ± 3.6 months) and compared the outcomes of MDR (n = 24, 13.0%) and SDR (n = 160, 87.0%) in PEVI. Primary endpoints included air kerma, dose area product (DAP), fluoroscopy time, and contrast use. Secondary endpoints included all-cause mortality, cardiac mortality, acute myocardial infarction, acute kidney injury, stroke, repeat revascularization, vessel dissection/perforation, major adverse limb event, access site complications, and composite of complications. Results: For MDR (68 ± 10 years, mean follow-up of 4.3 ± 5.2 months), the primary endpoints were significantly less than SDR (65 ± 12 years, mean follow-up of 3.8 ± 3.2 months; p < 0.001). Regarding the secondary endpoints, one vessel dissection occurred using MDR, while 36 total complications occurred with SDR (p = 0.037). Conclusions: PEVI using MDR was safe and efficacious. MDR showed a significant decrement in radiation parameters and fluoroscopy time. Therefore, MDR can serve as an effective alternative for PEVI in acute or critical limb ischemia. Full article

The growing mismatch between ecosystem service (ES) supply and demand underscores the importance of thoroughly understanding their spatiotemporal patterns and key drivers to promote ecological civilization and sustainable development at the regional level in China. This study investigates six key ES indicators across mainland China—habitat quality (HQ), carbon sequestration (CS), water yield (WY), sediment delivery ratio (SDR), food production (FP), and nutrient delivery ratio (NDR)—by integrating a suite of analytical approaches. These include a spatiotemporal analysis of trade-offs and synergies in supply, demand, and their ratios; self-organizing maps (SOM) for bundle identification; and interpretable machine learning models. While prior research studies have typically examined ES at a single spatial scale, focusing on supply-side bundles or associated drivers, they have often overlooked demand dynamics and cross-scale interactions. In contrast, this study integrates SOM and SHAP-based machine learning into a dual-scale framework (grid and city levels), enabling more precise identification of scale-dependent drivers and a deeper understanding of the complex interrelationships between ES supply, demand, and their spatial mismatches. The results reveal pronounced spatiotemporal heterogeneity in ES supply and demand at both grid and city scales. Overall, the supply services display a spatial pattern of higher values in the east and south, and lower values in the west and north. High-value areas for multiple demand services are concentrated in the densely populated eastern regions. The grid scale better captures spatial clustering, enhancing the detection of trade-offs and synergies. For instance, the correlation between HQ and NDR supply increased from 0.62 (grid scale) to 0.92 (city scale), while the correlation between HQ and SDR demand decreased from −0.03 to −0.58, indicating that upscaling may highlight broader synergistic or conflicting trends missed at finer resolutions. In the spatiotemporal interaction network of supply–demand ratios, CS, WY, FP, and NDR persistently show low values (below −0.5) in western and northern regions, indicating ongoing mismatches and uneven development. Driver analysis demonstrates scale-dependent effects: at the grid scale, HQ and FP are predominantly influenced by socioeconomic factors, SDR and WY by ecological variables, and CS and NDR by climatic conditions. At the city level, socioeconomic drivers dominate most services. Based on these findings, nine distinct supply–demand bundles were identified at both scales. The largest bundle at the grid scale (B3) occupies 29.1% of the study area, while the largest city-scale bundle (B8) covers 26.5%. This study deepens the understanding of trade-offs, synergies, and driving mechanisms of ecosystem services across multiple spatial scales; reveals scale-sensitive patterns of spatial mismatch; and provides scientific support for tiered ecological compensation, integrated regional planning, and sustainable development strategies. Full article

This paper presents a near real-time implementation of the Karhunen–Loève Transform (KLT) for Radio Frequency Interference (RFI) mitigation in microwave radiometry. KLT is a powerful, data-adaptive technique capable of adjusting to various signal types by estimating the covariance matrix of the incoming signal and segmenting its eigenvectors to form an effective RFI basis. In this paper, the KLT is evaluated with real signals in laboratory conditions, aiming to characterize its performance in realistic conditions. To that effect, the dual Rx/Tx capability of a Pluto SDR is used to generate and capture RFI. The main mitigation metrics are computed for the KLT and other commonly used mitigation methods. In addition, while previous studies have shown the effectiveness of offline processing of recorded I/Q data, real-time mitigation is often necessary. Given the computational cost of eigendecomposition, this work introduces a low-complexity solution using the “economy covariance” approach alongside asynchronous covariance decomposition. The proposed implementation, realized within the GNU Radio framework, demonstrates the practical feasibility of real-time KLT-based mitigation and underscores its potential for improving signal integrity in digital radiometers operating under dynamic RFI conditions. Full article

An analog joint source-channel coding (AJSCC) based on Shannon–Kotel’nikov (S-K) mapping transmitting discrete-time encoded samples in orthogonal frequency division multiplexing (OFDM) systems over wireless channel has exhibited excellent performance. However, the phenomenon of carrier frequency offset (CFO) caused by the frequency mismatch between the transmitter’s and receiver’s local oscillators often exists in actual scenarios; thus, in this paper the performance of AJSCC-OFDM with CFO is analyzed and the S-K mapping is optimized. A joint optimization strategy is developed to maximize the signal-to-distortion ratio (SDR) subject to CFO constraints. Considering that the optimized AJSCC-OFDM strategies will change the amplitude distribution of encoded symbol, the peak-to-average power ratio (PAPR) characteristics under different AJSCC parameters are also analyzed. Full article

Split-shaft wind turbines decouple the turbine’s shaft from the generator’s shaft, enabling several modifications in the drivetrain. One of the significant achievements of a split-shaft drivetrain is the reduction in size of the excitation circuit. The grid-side converter is eliminated, and the rotor-side converter can safely reduce its size to a fraction of a full-size excitation. Therefore, this low-power-rated converter operates at low voltage and handles regular operations well. However, fault conditions may expose weaknesses in the converter and push it to its limits. This paper investigates the effects of the reduced-size rotor-side converter on the voltage ride-through capabilities required from all wind turbines. Four different protection circuits, including the active crowbar, active crowbar along a resistor–inductor circuit (C-RL), series dynamic resistor (SDR), and new-bridge fault current limiter (NBFCL), are employed, and their effects are investigated and compared. Wind turbine controllers are also utilized to reduce the impact of faults on the power electronic converters. One effective method is to store excess energy in the generator’s rotor. The proposed low-voltage ride-through strategies are simulated in MATLAB Simulink (2022b) to validate the results and demonstrate their effectiveness and functionality. Full article

In wireless communications and radio frequency courses, Software-Defined Radios (SDRs) offer students hands-on experience with software-based signal processing on programmable hardware platforms such as Field Programmable Gate Arrays (FPGAs). While some remote SDR laboratories enable students to access real hardware, they typically lack support for Partial Reconfiguration (PR)—a powerful FPGA capability that allows sections of a design to be reconfigured at runtime without disrupting the main system operation. This capability enhances real-time adaptability and optimizes resource utilization, making it highly relevant for modern SDR applications. This study addresses this gap by extending an existing SDR remote lab to support PR, enabling students to explore reconfigurable hardware design within a remote learning environment. Two integration architectures were developed: one based on a graphical user interface (UI) and another utilizing a command-line workflow, both accessible via a web browser. Preliminary experiments using Red Pitaya SDR platforms—reportedly the first use of these devices for educational PR exploration—examined the impact of PR on logic resource utilization and total power consumption across three levels of design complexity. These results were compared to equivalent static FPGA designs performing the same functionality without PR. By making PR experimentation accessible through a remote platform, this work enhances STEM education by bridging advanced FPGA techniques with practical learning. It will equip students with industry-relevant skills for developing agile, resource-efficient wireless systems and foster a deeper understanding of adaptive hardware design. Full article

This study presented an autonomous shield-cutting end-effector for maize surrounding weeding (SEMSW), addressing the challenges of the low weed removal rate (WRR) and high seedling damage rate (SDR) in northern China’s 3–5 leaf stage maize. The SEMSW integrated seedling positioning, robotic arm control, and precision weeding functionalities: a seedling positioning sensor identified maize seedlings and weeds, guiding XYZ translational motions to align the robotic arm. The seedling-shielding anti-cutting mechanism (SAM) enclosed crop stems, while the contour-adaptive weeding mechanism (CWM) activated two-stage retractable blades (TRWBs) for inter/intra-row weeding operations. The following key design parameters were determined: 150 mm inner diameter for the seedling-shielding disc; 30 mm minimum inscribed-circle for retractable clamping units (RCUs); 40 mm ground clearance for SAM; 170 mm shielding height; and 100 mm minimum inscribed-circle diameter for the TRWB. Mathematical optimization defined the shape-following weeding cam (SWC) contour and TRWB dimensional chain. Kinematic/dynamic models were introduced alongside an adaptive sliding mode controller, ensuring lateral translation error convergence. A YOLOv8 model achieved 0.951 precision, 0.95 mAP50, and 0.819 mAP50-95, striking a balance between detection accuracy and localization precision. Field trials of the prototype showed 88.3% WRR and 2.2% SDR, meeting northern China’s agronomic standards. Full article

Concrete in cold areas is often subjected to a freeze–thaw cycle period, and a harsh environment will seriously damage the structure of concrete and shorten its life. The frost resistance of concrete is primarily evaluated by relative dynamic elastic modulus and mass loss rate. To predict the frost resistance of concrete more accurately, based on the four ensemble learning models of random forest (RF), adaptive boosting (AdaBoost), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost), this paper optimises the ensemble learning models by using a dynamic multi-stage optimisation algorithm (DMSOA). These models are trained using 7090 datasets, which use nine features as input variables; relative dynamic elastic modulus (RDEM) and mass loss rate (MLR) as prediction indices; and six indices of the coefficient of determination (R²), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and standard deviation ratio (SDR) are selected to evaluate the models. The results show that the DMSOA-CatBoost model exhibits the best prediction performance. The R² of RDEM and MLR are 0.864 and 0.885, respectively, which are 6.40% and 11.15% higher than those of the original CatBoost model. Moreover, the model performs better in error control, with significantly lower MSE, RMSE, and MAE and stronger generalization ability. Additionally, compared with the two mainstream optimisation algorithms (SCA and AOA), DMSOA-CatBoost also has obvious advantages in prediction accuracy and stability. Related work in this paper has a certain significance for improving the durability and quality of concrete, which is conducive to predicting the performance of concrete in cold conditions faster and more accurately to optimise the concrete mix ratio whilst saving on engineering cost. Full article

International students are an important group at German higher education institutions. Yet few studies have considered the psychosocial situation of these students. The present study combines the study demands-resources model (SD-R model) and the acculturation framework to adapt the former to the specific situation of international students, and examines the effects of study demands, institutional resources, individual resources (acculturation orientations, i.e., affective and cognitive orientations towards the home and host country), stress, and engagement on their life satisfaction as one indicator of well-being. We used cross-sectional survey data from 503 international students across more than 20 higher education institutions. Latent structural equation models showed that lower study demands were associated with lower stress and higher student engagement, which in turn predicted greater life satisfaction. Although higher institutional resources also related to increased engagement and, thus, higher life satisfaction, they were unexpectedly linked to increased stress, which was in turn associated with lower life satisfaction. Additionally, the expected positive effect of affective host country orientation on life satisfaction, mediated through perceived stress and engagement, was confirmed. The consideration of acculturation orientations in the SD-R model helps to understand the specific conditions of well-being amongst international students in Germany. Full article

Blind source separation (BSS) of underwater acoustic mixed signals aims to improve signal clarity by separating noise components from aliased underwater signal sources. This enhancement directly increases target detection accuracy in underwater acoustic perception systems, particularly in scenarios involving multi-vessel interference or biological sound coexistence. Deep learning-based BSS methods have gained wide attention for their superior nonlinear modeling capabilities. However, existing approaches in underwater acoustic scenarios still face two key challenges: limited feature discrimination and inadequate robustness against non-stationary noise. To overcome these limitations, we propose a novel Robust Cross-Band Network (RCBNet) for the BSS of underwater acoustic mixed signals. To address insufficient feature discrimination, we decompose mixed signals into sub-bands aligned with ship noise harmonics. For intra-band modeling, we apply a parallel gating mechanism that strengthens long-range dependency learning so as to enhance robustness against non-stationary noise. For inter-band modeling, we design a bidirectional-frequency RNN to capture the global dependency relationships of the same signal across sub-bands. Our experiment demonstrates that RCBNet achieves a 0.779 dB improvement in the SDR compared to the advanced model. Additionally, the anti-noise experiment demonstrates that RCBNet exhibits satisfactory robustness across varying noise environments. Full article

The Lunar Augmented Navigation Service (LANS) is the lunar equivalent of GNSS for future lunar explorations. It offers users accurate position, navigation, and timing (PNT) capabilities on and around the Moon. The Augmented Forward Signal (AFS) is a standardized signal structure for LANS, and its recommended standard was published online on 7 February 2025. This work presents software-defined radio (SDR) implementations of the LANS AFS simulator and receiver, which were rapidly developed within a month of the signal specification release. Based on open-source GNSS software, including GPS-SDR-SIM and Pocket SDR, our system provides a valuable platform for future algorithm research and hardware-in-the-loop testing. The receiver can operate on embedded platforms, such as the Raspberry Pi 5, in real-time. This feature makes it suitable for lunar surface applications, where conventional PC-based SDR systems are impractical due to their size, weight, and power requirements. Our approach demonstrates how open-source SDR frameworks can be rapidly applied to emerging satellite navigation signals, even for extraterrestrial PNT applications. Full article

This paper presents a novel, four-port, rectangular microstrip, inset-feed multiple-input and multiple-output (MIMO) antenna array, enhanced with metamaterials for improved gain and isolation, specifically designed for multi-operator 5G open radio access network (ORAN)-based indoor software-defined radio (SDR) applications. ORAN is an open-source interoperable framework for radio access networks (RANs), while SDR refers to a radio communication system where functions are implemented via software on a programmable platform. A 3 × 3 metamaterial (MTM) superstrate is placed above the MIMO antenna array to improve gain and reduce the mutual coupling of MIMO. The proposed MIMO antenna operates over a 300 MHz bandwidth (3.5–3.8 GHz), enabling shared infrastructure for multiple operators. The antenna’s dimensions are 75 × 75 × 18.2 mm³. The antenna possesses a reduced mutual coupling less than −30 dB and a 3.5 dB enhancement in gain with the help of a novel 3 × 3 MTM superstrate 15 mm above the radiating MIMO elements. A performance evaluation based on simulated results and lab measurements demonstrates the promising value of key MIMO metrics such as a low envelope correlation coefficient (ECC) < 0.002, diversity gain (DG) ~10 dB, total active reflection coefficient (TARC) < −10 dB, and channel capacity loss (CCL) < 0.2 bits/sec/Hz. Real-world testing of the proposed antenna for ORAN-based sub-6 GHz indoor wireless systems demonstrates a downlink throughput of approximately 200 Mbps, uplink throughput of 80 Mbps, and transmission delays below 80 ms. Additionally, a walk test in an indoor environment with a corresponding floor plan and reference signal received power (RSRP) measurements indicates that most of the coverage area achieves RSRP values exceeding −75 dBm, confirming its suitability for indoor applications. Full article

This study elucidates potential genetic determinants and mechanisms involved in the synergistic effects of daptomycin (DAP) + fosfomycin (FOF) combination therapy. Among 33 clinically derived DAP-susceptible (S)/DAP-resistant (R) isogenic strain pairs, mutations in the mprF gene occurred in 30/33 DAP-R strains, including polymorphisms of L826F (33%) or T345A/L/I (15%). Strain variants of DAP-S CB1483 serially passaged in vitro for 10 days in DAP +/− FOF identified a key non-synonymous mutation in mprF (L826F) only in the DAP monotherapy arm. Interestingly, passage in FOF alone or DAP + FOF prevented the emergence of this mprF mutation following 10-day passage. This L826F mprF polymorphism, associated with a “gain-in-function” phenotype, exhibited increased amounts of lysyl-phosphatidylglycerol (L-PG) in the cell membrane (CM). Transcriptomics revealed a relatively modest number (~10) of distinct genes that were significantly up- or downregulated (≥2 log fold) in both the DAP-S and DAP-R strain pairs upon DAP + FOF exposures (vs. DAP or FOF alone). Of note, DAP + FOF decreased expression of lrgAB and sdrE and increased the expression level of fosB. In a rabbit infective endocarditis (IE) model, the DAP-R CB185 strain treated with DAP +/− FOF showed significantly reduced lrgB expression in vegetations compared with DAP treatment alone. Overall, these findings indicate that DAP + FOF therapy impacts MRSA through multiple specific mechanisms, enhancing bacterial clearance. Full article