Search Results (1,142)

A disconnect remains between high-fidelity physical-characteristic simulation and upper-level validation in RF system design. High-fidelity simulations can accurately characterize key physical effects, such as frequency response, noise, and nonlinearity, but their results are difficult to directly transform into executable models for upper-level validation. In contrast, upper-level validation often relies on idealized or empirical parameters rather than real hardware characteristics. To address this issue, this paper proposes a dataflow-driven behavioral modeling method for RF systems, with system input–output characteristics as the modeling core. A behavioral model is constructed using characteristic blocks representing frequency response, noise, coupling, nonlinearity, and phase shift. Model parameters are configured from high-fidelity simulation results and/or hardware measurement data, thereby establishing a parameter-transfer path from physical-characteristic results to the executable behavioral model. Driven by baseband-equivalent input data streams, the model generates output data streams containing key physical effects and provides a reusable RF-link model for upper-level validation. The proposed method is instantiated and validated on the receive (Rx) channel of an X-band eight-channel phased-array transmit/receive module. Comparisons with circuit-level benchmark results demonstrate that the proposed method can effectively inherit underlying physical characteristics and exhibits good accuracy and practical feasibility. Full article

Background/Objectives: Pseudomonas aeruginosa is a major cause of healthcare-associated infections, and the global rise of multidrug-resistant (MDR) strains has created an urgent need for alternative therapeutics. R- and F-type pyocins are phage tail-like bacteriocins that selectively kill P. aeruginosa by binding to lipopolysaccharide (LPS) receptors. We characterized O-serotype distribution and pyocin susceptibility among clinical isolates from central Taiwan to evaluate their therapeutic potential. Methods: A total of 109 ICU-derived P. aeruginosa isolates were analyzed. O-serotypes were determined by PCR, and pyocin gene carriage was confirmed by sequencing. Purified R1, R2, R5, F1, F2, F4, F7, and F12 pyocins were tested using spot assays. LPS profiles were examined by SDS-PAGE to explore structural correlates of resistance. Synergistic effects of combined R- and F-type pyocins were assessed in MDR isolates. Results: The most prevalent serotypes were O6 (23.9%), O2/O5/O16/O18/O20 (20.2%), O1 (16.5%), and O11/O17 (15.6%). Susceptibility was strongly serotype-dependent: O1 and O6 were highly sensitive to both pyocin types, whereas the O2/O5/O16/O18/O20 group showed marked resistance. SDS-PAGE demonstrated that resistant isolates possessed densely packed long-chain O-antigens, likely shielding LPS core receptors from pyocin binding. F-type pyocins exhibited bactericidal activity comparable to R-types, and R/F pyocin cocktails produced synergistic killing against MDR isolates. Conclusions: These findings provide an updated serotype profile of P. aeruginosa in Taiwan and highlight the importance of LPS structural variability in pyocin susceptibility. These results underscore the potential of pyocin-based cocktails as a promising precision-medicine strategy to inhibit the planktonic growth and biofilm formation of multidrug-resistant P. aeruginosa isolates. Full article

Accurate path-loss models are essential for planning reliable wireless networks in underground mines, yet existing characterization studies rely on specialized channel sounders and vector network analyzers costing tens of thousands of dollars, placing them beyond the reach of most mine operators. This paper demonstrates that LoRa transceivers costing approximately US $15 per node can serve as a self-contained path-loss measurement instrument, logging the received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) directly to a CSV file over a standard USB serial connection. A measurement campaign conducted at the Missouri S&T Experimental Mine on 31 March 2026 collected 4801 packets across four distinct underground canonical primitives: straight tunnel, T-junction, vertical shaft, and post-bend NLoS gallery at distances of 5 to 60 m using Waveshare Pico-LoRa-SX1262 boards operating at 915 MHz. The results reveal a pronounced two-zone propagation structure, including a line-of-sight (LoS) zone with a negative path-loss exponent of −0.34, confirming tunnel waveguide gain up to 25 m, followed by a steep NLoS zone with an exponent of 13.0 after a 24.0 dB bend diffraction loss. Environment-specific measurements quantify a 5.5 dB junction excess loss and a 29.5 dB shaft excess loss relative to a straight-tunnel reference. Spreading factor sensitivity tests across SF7, SF9, and SF12 confirm that RSSI measurements are consistent to within 2 dB across all SFs, validating the measurement methodology. The resulting four-zone path-loss model provides mine network planners with parameters sufficient for LoRa link budget design and relay node placement without any specialized RF instrumentation. Full article

Background/Objectives: Type 2 diabetes (T2D)-associated sarcopenia is characterized by impaired insulin signaling, lipotoxicity, oxidative stress, and progressive muscle loss. Although liraglutide improves glucose control and reduces lipid burden, its ability to preserve muscle integrity under diabetic lipotoxic conditions remains limited. This study investigated whether β-hydroxy-β-methylbutyrate (HMB) could enhance liraglutide-mediated protection against high-glucose plus free fatty acid (HG+FFA)-induced injury in skeletal muscle cells. Methods: Differentiated C2C12 myotubes were exposed to HG+FFA to establish a sublethal lipotoxic model and treated with liraglutide, HMB, or their combination. Cell viability, lipid accumulation, myotube morphology, insulin signaling, glucose uptake, mitochondrial function, reactive oxygen species (ROS), antioxidant gene expression, and atrophy-related signaling were assessed. Results: HG+FFA induced marked lipid droplet accumulation, impaired insulin signaling, reduced glucose uptake, disrupted mitochondrial membrane potential, increased ROS production, suppressed antioxidant gene expression, and promoted an atrophic phenotype characterized by increased atrogin-1 and MuRF1 and reduced myogenic markers. Liraglutide alone reduced large lipid droplets and partially improved insulin signaling but showed limited efficacy in preserving the myotube phenotype. HMB alone exerted modest effects on lipid accumulation but preserved myotube area. Notably, combined HMB and liraglutide treatment more effectively reduced lipid burden, restored insulin signaling and glucose uptake, attenuated mitochondrial dysfunction and oxidative stress, restored antioxidant gene expression, and preserved MyHC-positive area and myotube diameter while suppressing atrogin-1/MuRF1 activation. These protective effects were largely attenuated by rapamycin, indicating at least partial dependence on mTOR-associated signaling. Conclusions: Overall, HMB and liraglutide exert complementary protective effects against diabetic lipotoxic and atrophic stress, supporting the potential utility of this combination strategy for T2D-associated sarcopenia. Full article

The reflection coefficient measurement of the RF signal generator output is clear when the signal generator output is turned off, as no interfering signal is present. However, measuring the reflection coefficient while the signal generator output is turned on creates complexity, as the generator’s output power can interfere with the reflected signal. A vector network analyzer (VNA) is the reference instrument for measuring the reflection coefficient, capturing both the magnitude and phase of scattering parameters. For measuring the active output of a signal generator, the signals created by the generator and the VNA must be isolated to prevent signal mixing and interference. This paper proposes a unique method to measure the output reflection coefficient of an RF signal generator when the output is on, using a VNA configured for one port reflection coefficient measurement. The method involves tuning the VNA receiver to a frequency slightly offset to the generator’s output. Simultaneously, selecting a narrow intermediate frequency bandwidth (IFBW) reduces the receiver’s noise floor and also eliminates out-of-band interference. As a result, the VNA and the generator operate in different frequency bands to avoid interferences between them, enabling accurate magnitude and phase measurements. To automate the process, a Windows-based software has been developed. This software automates the measurement sequence, controls generator power levels and VNA sweep parameters, captures both the magnitude and phase of the reflection coefficient, and records the result data. It also supports measurement at different output power levels, enabling characterization across a wide range of operating conditions. Full article

3D Gaussian Splatting (3DGS) has emerged as a cornerstone technique for 3D asset acquisition. However, existing copyright protection methods for 3DGS predominantly rely on embedding watermarks directly into Gaussian primitives, which inevitably degrades rendering quality. To address this issue, this paper proposes a zero-watermarking framework. By directly mapping the inherent features of rendered images to copyright information without modifying Gaussian parameters, the framework achieves perfect visual fidelity. Conventional image zero-watermarking maps features of a single image to a dedicated watermark. In contrast, our method guarantees mapping consistency: features of rendered images from any unknown viewpoint can be mapped to the same copyright identifier. To address this cross-view consistency challenge, we introduce an uncertainty-guided strategy that scores individual pixels to guide the decoder to mine shared features across multiple perspectives. This strategy enables accurate watermark retrieval even from novel viewpoints. Extensive experiments on the Blender, LLFF, and MipNeRF-360 datasets demonstrate that our method achieves superior performance, characterized by high message capacity, strong adversarial robustness, and a low false positive rate (FPR), while fully maintaining the integrity of the original 3DGS model. Full article

The increasing activity of unmanned aerial vehicles (UAVs) has intensified the demand for reliable and computationally efficient methods for passive radio-frequency (RF) signal detection. In practical RF monitoring scenarios, the environment is often non-stationary and affected by varying noise conditions. Under such circumstances, classical energy-based detectors are sensitive to noise uncertainty, while more robust approaches, such as cyclostationary analysis, require substantially higher computational resources. This work presents a burst-aware cascade method for UAV RF signal presence detection that explicitly addresses this trade-off. The proposed framework combines fast energy-based screening with temporal burst aggregation, applying spectral correlation function (SCF) analysis selectively and only when sustained signal activity is indicated. Detection is performed on fixed-length RF signal chunks, while additional segment-level duration constraints are used to characterize sustained transmissions. The method is evaluated using the publicly available DroneRF dataset and compared against six baseline detectors, including fixed-threshold energy, wavelet-based, blind cyclostationary, two adaptive energy detector variants, and a lightweight convolutional neural network. Experimental results confirm that chunk-level detection remains difficult for all considered methods. Temporal aggregation across longer intervals yields a substantial improvement: the cascade achieves $P_d$ = 1.000 and AUC = 1.000 at the segment level, matching exhaustive cyclostationary detection while reducing per-segment processing time by a factor of 2.46. An additional result is that burst-level concatenation prior to SCF estimation provides implicit coherent integration, preserving $P_d$ = 1.000 at signal amplitude reductions of up to −20 dB where standalone detectors degrade to $P_d$ = 0.995. Overall, burst-aware cascade architectures offer a practical and interpretable approach to RF-based UAV monitoring, providing a well-grounded compromise between detection reliability and computational efficiency under realistic operating conditions. Full article

National forest parks play an important role in maintaining the integrity of ecosystems, the sustainability of biodiversity, and the improvement of ecological service functions. Aboveground carbon storage (AGC) is an important indicator for evaluating forest ecosystem functions. Qianjiangyuan–Baishanzu National Park, located in the southern part of Lishui City, serves as a typical representative of the mid-subtropical evergreen broad-leaved forest ecosystem. It is characterized by high biodiversity and serves as a concentration area for rare and endangered species. Therefore, assessing the spatiotemporal dynamics of forest AGC in the typical subtropical forests of Qianjiangyuan–Baishanzu National Park is of great scientific significance. Taking Qianjiangyuan–Baishanzu National Park as a case study, this research utilized three periods of Landsat satellite remote sensing data (2009, 2014, and 2019) alongside contemporaneous ground-based AGC survey samples. Feature variables were extracted and subsequently screened using the Boruta algorithm. There were three algorithms, including least squares (LS), support vector regression (SVR), and random forest (RF), constructed to estimate forest AGC. The optimal AGC estimation model was selected, and the spatiotemporal variation characteristics of forest AGC within the national park were systematically analyzed. Research has shown that (1) texture features are important parameters for constructing forest AGC estimation models, accounting for up to 71%, with the 7 × 7 window having the greatest impact. (2) All three models can achieve high accuracy in estimating the forest AGC and its spatial distribution in Qianjiangyuan Baishanzu National Park. Among them, the RF model has the highest overall accuracy in estimating forest AGC, with a training set R² of 0.938, RMSE of 5.550 Mg/ha, rRMSE of 12.517%, a test set R² of 0.954, RMSE of 4.653 Mg/ha, and rRMSE of 10.087%. (3) The distribution of forest AGC in Qianjiangyuan Baishanzu National Park shows significant spatial heterogeneity, with higher carbon storage in the central, southern, and southeastern regions, while the northern region has relatively lower carbon storage. From 2009 to 2019, the forest AGC in the Qianjiangyuan–Baishanzu National Park exhibited a steady annual increase, with AGC density rising from 37.64 Mg/ha to 66 Mg/ha and total AGC stock increasing from 2.16 Tg C to 4.36 Tg C. These findings provide precise data support and a scientific basis for decision-making regarding differentiated ecological carbon enhancement and functional zone management within the national park. Full article

Background: Metabolic syndrome (MS), characterized by a constellation of interrelated cardiometabolic abnormalities, markedly amplifies cardiovascular risk. Despite the high prevalence of atherosclerotic cardiovascular disease (ASCVD) in the Middle East, evidence regarding the burden and determinants of MS in this high-risk population remains limited. This study aimed to estimate the prevalence of MS and identify its independent predictors among Middle Eastern patients with established ASCVD. Methods: This comprehensive analysis integrated data from two complementary sources: a prospective cohort derived from the Jordan SMuRF-less Study, which enrolled adults (≥18 years) with confirmed ASCVD across nine centers in Jordan, and a pooled retrospective dataset from six regional cardiovascular registries. Standardized case report forms were used to collect demographic, clinical, and laboratory data. Participants were stratified according to the number of standard modifiable risk factors (SMuRFs) into three categories (0, 1–2, and 3–4 SMuRFs). Multivariable logistic regression analysis was conducted to determine independent predictors of MS. Results: Among 1016 patients with ASCVD, MS was present in 42.7% of the cohort. The prevalence of MS demonstrated a significant graded increase with higher SMuRF burden, rising from 2.2% in patients without SMuRFs to 28.3% in those with one to two SMuRFs and 62.2% in those with three to four SMuRFs (p < 0.001). Patients with MS were significantly older and exhibited higher body mass index and triglyceride levels, lower high-density lipoprotein cholesterol, and a greater prevalence of hypertension, diabetes mellitus, dyslipidemia, chronic kidney disease, and heart failure (all p < 0.001). Independent predictors of MS included advanced age, diabetes mellitus, hypertension, chronic kidney disease, heart failure, elevated body mass index, and increased triglyceride levels. In contrast, higher HDL cholesterol and smoking were inversely associated with MS. Conclusions: MS is highly prevalent among Middle Eastern patients with ASCVD and is strongly associated with cumulative SMuRF burden in a graded manner. These findings highlight the urgent need for targeted, region-specific strategies focusing on early identification and comprehensive management of cardiometabolic risk in this vulnerable population. Full article

This work presents the realization of a compact and embedded impedance-based sensor system for the characterization of lithium-ion batteries by means of electrical impedance spectroscopy (EIS). The analog magnitude-ratio and phase-difference detection (MRPDD) method is implemented and extended through a generalized formulation that models the shunt element as a frequency-dependent impedance and compensates the parasitic contributions of the printed circuit board. This reformulation corrects magnitude and phase errors introduced by the measurement hardware without increasing the overall complexity. The prototype comprises two main functional blocks: current-mode excitation and voltage-mode measurement. The excitation stage uses an operational transconductance amplifier and a power MOSFET to generate a voltage-controlled current source, whereas the sinusoidal voltage signal is generated by means of a direct digital synthesizer. The measurement chain relies on differential acquisition using instrumentation amplifiers and analog magnitude/phase detection based on the AD8302 vector detector under microcontroller control. The proposed method has been first validated by simulations using both a linear RC equivalent model and an extended Randles-type battery-equivalent model, and then experimentally characterized using a linear RC equivalent model of the device under test. Measurements show that the generalized formulation recovers the ideal impedance response in the presence of parasitic effects, both in the shunt device and in the printed circuit board. In the experimental validation with the RC model, a magnitude error of 1.65% is obtained at 1 kHz, which is adopted as the upper frequency limit for battery characterization, even though operation up to 10 kHz is possible. Phase measurements revealed that the input capacitive coupling of the vector detector, conceived for operation in the RF range, requires an adaptation for appropriate operation in the intended frequency range. The prototype has been also applied to the characterization of a commercial lithium-ion 18650 cell, enabling the measurement of battery impedance and the analysis of its dependence on the state-of-charge and on the discharge current. Full article

Accurate assessment of soil moisture is essential for enhancing irrigation efficiency and promoting sustainable agriculture. This study was conducted at Punjab Agricultural University, Ludhiana (PAU), to investigate the spatial and depth-wise variability of soil moisture across 30 field sites by using field measurements, geospatial-based (inverse distance weighting: IDW) interpolation, and machine learning techniques. Soil moisture was recorded at four depth intervals, including 0–15 cm, 15–30 cm, 30–45 cm, and 45–60 cm. The surface layer (0–15 cm) exhibited the highest variability due to evaporation and irrigation timing, with values ranging from 4.5% to 16.0%. Deeper layers showed more stable moisture retention, particularly at sites with intensive irrigation and crop cover, such as L11 (wheat), L22 (Gobhi Sarson), and L25 (wheat), where the moisture levels exceeded 14% at 45–60 cm depth, supporting suitability for deep-rooted crops. Supervised machine learning models, namely decision tree (DT), random forest (RF), and logistic regression (LR), were employed to classify soil moisture into low, medium, and high categories. The highest classification accuracy (88.9%) was achieved by the decision tree at 30–45 cm and logistic regression at 15–30 cm. Shallow layers exhibited frequent misclassification between medium and high classes, indicating surface-induced variability. Unsupervised clustering using K-means (k = 4) and hierarchical methods effectively delineated distinct soil moisture zones aligned with land use, irrigation history, and crop cover. The combination of geospatial analysis, depth-specific field data, and machine learning models provides an integrated framework for precision soil moisture assessment. This approach supports site-specific irrigation scheduling and water resource optimization, which are particularly critical for groundwater-stressed regions like Punjab. The novelty of this study lies in integrating depth-specific field-based soil moisture observations with geospatial interpolation and machine learning-based classification and clustering approaches to improve subsurface moisture characterization for precision irrigation management. Full article

Gallium oxide is an ultra-wide bandgap semiconductor with excellent opto-electronic properties, making it a highly promising material for a wide range of applications and devices. In this article, we report how the optical, morphological, structural, and compositional properties of $\beta$-Ga₂O₃ thin films deposited by RF Sputtering on silicon substrates are affected by thermal treatments. Ellipsometric spectra recorded at multiple angles of incidence from several samples subjected to thermal annealing in the range of 550–1000 °C were analyzed to extract the optical functions using appropriate multilayer models. This analysis is complemented by compositional, structural, and morphological characterization techniques. We observed two main stages of crystallization with increasing annealing temperature; up to 700 °C, there is an increase in density and then, for 700–1000 °C, there is an improvement in crystallinity. While the refractive index increases continuously throughout this process, we found that the polarizability of the samples decreases in the first stage and increases in the second. These observations demonstrate that thermal treatments are a powerful tool to tune the optical properties of Ga₂O₃ thin films for device applications. Full article

High-resolution three-dimensional (3D) radar imaging of non-coherent point target clusters faces significant challenges, particularly severe angular glint induced by the simultaneous presence of dual targets or co-channel interference (CCI) within the antenna mainlobe. Conventional monopulse systems often struggle to resolve such overlapping sources, particularly under conditions of high power disparity between signal components. To overcome the Rayleigh resolution limit, this paper proposes a polarimetric 3D imaging framework for three-channel monopulse radar by leveraging joint polarimetric-angular diversity. By exploiting the intrinsic instability of spatial parameter estimates induced by snapshot-to-snapshot echo envelope fluctuations, a cost function based on fluctuation minimization is constructed. Furthermore, an optimized oblique projection (OP) strategy is developed to decouple overlapped echoes in the joint domain, thereby effectively extracting stable angular features of non-coherent sources under various stochastic scattering scenarios (e.g., Swerling models). Extensive simulations demonstrate that, compared with traditional MPV, Seung, and Blair methods, the proposed approach consistently achieves superior estimation precision and robustness, especially in challenging scenarios characterized by low signal-to-noise ratios (SNR), limited snapshots, and restricted polarimetric diversity. Moreover, experimental validation using real-world data from a 45-m civilian vessel and an active non-cooperative radio frequency (RF) source confirms the practical effectiveness of the algorithm in resolving extended targets in the presence of strong non-coherent background emissions. This work provides a reliable solution for high-fidelity 3D imaging of point target clusters in environments characterized by dense targets and complex electromagnetic interference. Full article

In vitro cultured plant calli, induced through dedifferentiation, are colonized by diverse endophytes. Most of these endophytes, being substantially inherited from the mother plant and highly dependent on the host’s internal ecological niche, are termed host-dependent endophytes (HDEs). Due to their close association with their hosts, HDEs exhibit heritable characteristics. However, our current understanding of plant HDEs and their effects on the host plant is limited. In this study, we characterized the composition and potential functions of the endophytic microbiota in grapevine calli derived from different varieties and organs corresponding to Cabernet Sauvignon berry flesh (CF), Rose Honey berry flesh (RF), and Rose Honey shoot tip (RS) using high-throughput sequencing and bioinformatics. Our results showed that the genotype and organotype of the explant did not affect the alpha diversity of endophytes in callus, but were associated with differences in beta diversity and community structure of the endophytic microbiota. Different types of grapevines calli inherited distinct endophytes from their mother plants, whereas sharing a conservative core endophytic microbiota consisting of a small number of amplicon sequence variants (ASVs) with high relative abundances (bacteria: 38 ASVs ranging from 79 to 92%; fungi: 9 ASVs ranging from 32 to 58). Prediction analyses using revealed conserved functional traits of the endophytic microbiota across callus types, including a core suite of bacterial adaptive phenotypes, stable central metabolism dominated by oxidative phosphorylation, and uniformly structured fungal communities dominated by saprotrophs and pathotrophs, while consistently containing yeast-form fungi. Although minor variations such as elevated trait abundance in the CF group were noted, no statistically significant functional divergence was observed, demonstrating that the endophytic microbiota of grapevine callus maintains a conserved functional profile across different types. Collectively, this study provides a methodological framework for investigating plant HDEs and offers new insights into host-endophyte interactions at the cellular level. Full article

Background: Pancreatic neuroendocrine tumors (pNETs) represent a heterogeneous group of neoplasms characterized by variable biological behavior and clinical outcomes. Multiple therapeutic strategies have been investigated, including surgery, targeted therapies, peptide receptor radionuclide therapy, and systemic treatments. The present study aimed to summarize survival-related outcomes reported across studies investigating the management of pNETs. Methods: A systematic review of the literature was conducted including studies reporting clinical outcomes in patients with pancreatic neuroendocrine tumors. A total of 27 studies were included in the qualitative analysis. Survival-related outcomes, such as progression-free survival (PFS), recurrence-free survival (RFS), and recurrence rates, were extracted. Studies reporting quantitative survival values were included in the meta-analytical component. A random-effects model was applied, and a forest plot was generated to summarize the reported outcomes. Results: Reported survival outcomes varied substantially across studies. Median PFS values ranged from approximately 5.6 to 86.5 months, while several surgical series reported 5-year overall survival rates exceeding 90%. Recurrence rates following surgical resection ranged from approximately 12% to 26% in some cohorts. The pooled estimate derived from the meta-analytical model was 32.22 (95% CI: 15.65–48.80). Conclusions: The analysis summarizes survival-related outcomes reported in studies investigating pancreatic neuroendocrine tumors and provides a quantitative overview of the reported progression-related endpoints across the analyzed literature. Full article