Search Results (490)

The growing need for environmentally friendly separation processes has motivated the search for alternative solvents to petroleum-derived chemicals for the recovery of biosynthesized products. Although effective, conventional petroleum-based solvents pose major environmental and sustainability concerns, including pollution, ecotoxicity, human health risks, and high costs and energy demands for recycling. Consequently, current research and industrial practice increasingly focus on their replacement with safer and more sustainable alternatives. This study investigates the use of natural oils (i.e., grapeseed, sweet almond, and flaxseed oils) as renewable, biodegradable, and non-toxic diluents in reactive extraction systems for the separation of 7-aminocephalosporanic acid (7-ACA). The combination of these oils with tri-n-octylamine (TOA) as extractant enabled high extraction efficiencies, exceeding 50%. The system comprising 120 g/L tri-n-octylamine in grapeseed oil, an aqueous phase pH of 4.5, a contact time of 1 min, and a temperature of 25 °C resulted in a 7-ACA extraction efficiency of 63.4%. Slope analysis suggests that complex formation likely involves approximately one molecule each of tri-n-octylamine and 7-ACA, although the apparent order of the amine is reduced in systems using natural oils. This study highlights the potential of natural oil-based reactive extraction as a scalable and environmentally friendly method for 7-ACA separation, aligning with the principles of green chemistry and environmental biotechnology. Full article

We propose a general and experimentally accessible framework to quantify transition timing in discrete quantum systems via the time-of-flow (TF) distribution. Defined from the rate of population change in a target state, the TF distribution can be reconstructed through repeated projective measurements at discrete times on independently prepared systems, thus avoiding Zeno inhibition. In monotonic regimes, it admits a clear interpretation as a time-of-arrival (TOA) or time-of-departure (TOD) distribution. We apply this approach to optimize time-dependent Hamiltonians, analyze shortcut-to-adiabaticity (STA) protocols, study non-adiabatic features in the dynamics of a three-level time-dependent detuning model, and derive a transition-based quantum speed limit (TF-QSL) for both closed and open quantum systems. We also establish a lower bound on temporal uncertainty and examine decoherence effects, demonstrating the versatility of the TF framework for quantum control and diagnostics. This method provides both a conceptual tool and an experimental protocol for probing and engineering quantum dynamics in discrete-state platforms. Full article

The successful launch of Korea Multipurpose Satellite-3/Advanced Earth Imaging Sensor System (KOMPSAT-3/AEISS) on 18 May 2012 allowed the Republic of Korea to meet the growing demand for high-resolution satellite imagery. However, like all satellite sensors, KOMPSAT-3/AEISS experienced temporal changes post-launch and thus requires ongoing evaluation and calibration. Although more than a decade has passed since launch, the KOMPSAT-3/AEISS mission and its multi-year data archive remain widely used. This study focused on the cross-calibration of KOMPSAT-3/AEISS with Sentinel-2A/Multispectral Instrument (MSI) by comparing the radiometric responses of the two satellite sensors under similar observation conditions, leveraging the linear relationship between Digital Numbers (DN) and top-of-atmosphere (TOA) radiance. Cross-calibration was performed using near-simultaneous satellite images of the same region, and the Spectral Band Adjustment Factor (SBAF) was calculated and applied to account for differences in spectral response functions (SRF). Additionally, Bidirectional Reflectance Distribution Function (BRDF) correction was applied using MODIS-based kernel models to minimize angular reflectance effects caused by differences in viewing and illumination geometry. This study aims to evaluate the radiometric consistency of KOMPSAT-3/AEISS relative to Sentinel-2A/MSI over Baotou scenes acquired in 2022–2023, derive band-specific calibration coefficients and compare them with prior results, and conduct a side-by-side comparison of cross-calibration and vicarious calibration. Furthermore, the cross-calibration yielded band-specific gains of 0.0196 (Blue), 0.0237 (Green), 0.0214 (Red), and 0.0136 (NIR). These findings offer valuable implications for Earth observation, environmental monitoring, and the planning and execution of future satellite missions. Full article

This paper studies the problem of locating wireless sensor networks (WSNs) based on time-of-arrival (TOA) measurements in mixed line of sight/non-line-of-sight (LOS/NLOS) environments. To mitigate the impacts of NLOS and improve performance both in positioning accuracy and computation time, we hereby propose an exhaustive method (i.e., EM). The EM method mainly consists of two processes. In the first process, all BSs are arranged into various combinations. For each combination, a solution and its corresponding residual vector can be obtained. For each combination, all BSs can be divided into two categories: BSs that participate in positioning and BSs that do not. Therefore, the above residual vector can also be divided into two categories in each group. In the second process, combining the comparison results of two residual vectors and the characteristics of NLOS errors, we propose a new criterion to find out solutions with only LOS-BSs. Then the final solution can be obtained by further processing these solutions. This method does not require any prior information regarding NLOS status, NLOS amplitude, or noise variance, and only needs three LOS-BSs. Numerical simulation results shows that our method greatly improves the accuracy and reduces the computation time compared to state-of-art methods. Full article

Indoor localization of fast-moving targets under asynchronous acoustic sensing is severely constrained by non-line-of-sight (NLOS) propagation and sparse anchor deployments. To overcome these limitations, we propose a trajectory reconstruction-based framework that simultaneously exploits time-of-arrival (ToA) and frequency-of-arrival (FoA) measurements. By embedding temporal continuity and motion dynamics into the localization model, we cast the problem as a constrained nonlinear least squares optimization over the entire trajectory rather than isolated snapshots. To efficiently solve this high-dimensional problem, we design an enhanced particle swarm optimization (PSO) algorithm featuring adaptive phase switching and noise-resilient updates. Simulation results under varying noise conditions show that our method achieves superior accuracy and robustness compared to conventional least squares estimators, especially for high-speed trajectories. Real-world experiments using a passive acoustic testbed further validate the effectiveness of the proposed framework, with over 90% of localization errors confined within 3 m. The method is model-driven, training-free, and scalable to asynchronous and anchor-sparse environments. Full article

The article presents the results of a study on 50/70 paving-grade bitumen modified with the bitumen recovered from two types of asphalt shingles: post-consumer asphalt shingles (TOAS) and manufacturing waste asphalt shingles (MWAS) at three dosage levels (15%, 30%, and 45% w/w). The evaluation included the basic properties of bitumen—its penetration and softening point—as well as rheological properties, such as its viscosity, fatigue life determined by the LAS method, and rutting resistance assessed using the MSCR test and FTIR analysis. In both cases, the results showed that an increase in the stiffness of the base bitumen was observed. An improvement in rutting resistance was also recorded, as evidenced by the reduction of J_nr3.2, along with an increase in fatigue life. A stronger stiffening effect was found in the case of the TOAS-derived bitumen, which is related to aging processes occurring during its service life. This suggests that the maximum allowable content of the additive should depend on the source of the reclaimed asphalt shingles, with MWAS being applicable in larger amounts without excessive deterioration of bitumen performance. The key contribution of this study is the demonstration that the MWAS and TOAS additives cannot be treated equally, as each affects the base bitumen differently. Full article

With the widespread deployment of 5G technology, high-precision positioning in global navigation satellite system (GNSS)-denied environments is a critical yet challenging task for emerging 5G applications, enabling enhanced spatial resolution, real-time data acquisition, and more accurate geolocation services. Traditional methods relying on single-source measurements like received signal strength information (RSSI) or time of arrival (TOA) often fail in complex multipath conditions. To address this, the positional encoding multi-scale residual network (PE-MSRN) is proposed, a novel deep learning framework that enhances positioning accuracy by deeply mining spatial information from 5G channel state information (CSI). By designing spatial sampling with multigranular data and utilizing multi-source information in 5G CSI, a dataset covering a variety of positioning scenarios is proposed. The core of PE-MSRN is a multi-scale residual network (MSRN) augmented by a positional encoding (PE) mechanism. The positional encoding transforms raw angle of arrival (AOA) data into rich spatial features, which are then mapped into a 2D image, allowing the MSRN to effectively capture both fine-grained local patterns and large-scale spatial dependencies. Subsequently, the PE-MSRN algorithm that integrates ResNet residual networks and multi-scale feature extraction mechanisms is designed and compared with the baseline convolutional neural network (CNN) and other comparison methods. Extensive evaluations across various simulated scenarios, including indoor autonomous driving and smart factory tool tracking, demonstrate the superiority of our approach. Notably, PE-MSRN achieves a positioning accuracy of up to 20 cm, significantly outperforming baseline CNNs and other neural network algorithms in both accuracy and convergence speed, particularly under real measurement conditions with higher SNR and fine-grained grid division. Our work provides a robust and effective solution for developing high-fidelity 5G positioning systems. Full article

Bitumen ages during production and in asphalt pavements, leading to structural issues and reduced durability of asphalt pavements. The alteration of bitumen’s viscoelastic properties, predominantly attributable to oxidation phenomena, is a hallmark of these processes. This study analyzed the use of a new generation of synthetic wax (SW_LC), which was selected for its low carbon footprint, ability to reduce binder viscosity, and ability to enable the production of WMA. Tall oil amidopolyamines (TOAs), a renewable raw material-based adhesive and aging inhibitor, was also used in this study. It compensates for the unfavorable effect of stiffening the binder with synthetic wax. SW_LC at concentrations of 1.0%, 1.5%, 2.0%, and 2.5% by mass in bitumen, in conjunction with TOAs at concentrations of 0.0%, 0.2%, 0.4%, and 0.6% by bitumen weight were tested at various concentrations. Short-term and long-term aging effects on penetration, softening point, and viscosity multiple creep and stress recovery tests (MSCR), oscillatory tests for the combined complex modulus |G*| and phase shift angle sin(δ) (DSR), and low-temperature characteristics S_m and m_value (BBR) were analyzed. The chemical composition of the binders was then subjected to Fourier Infrared Spectroscopy (FTIR) analysis, which enabled the determination of carbonyl, sulfoxide, and aromaticity indexes. These results indicated that the additives used inhibit the oxidation and aromatization reactions of the bitumen components. The optimal SW_LC and TOA content determined was 1.5% and 0.4% w/w, respectively. These additives reduce aging and positively affect rheological parameters. Full article

In this study, we describe the derivation and evaluation of Top of the Atmosphere (TOA) Shortwave Radiative (SWR) Fluxes from the Advanced Baseline Imager (ABI) sensor on the GOES-18 satellite. The TOA estimates use narrowband observations from ABI that are transformed to broadband (NTB), based on simulations and adjusted to total fluxes using Angular Distribution Models (ADMs). Subsequently, the GOES-18 estimates are evaluated against the Clouds and the Earth’s Radiant Energy System (CERES) data, the only observed SWR broadband flux dataset. The importance of agreement at the TOA is that most methodologies to derive surface SWR start with the satellite observation at the TOA. Moreover, information needed to compute radiative fluxes at both boundaries (TOA and surface) is needed for estimating the energy absorbed by the atmosphere. The methodology described was comprehensively evaluated, and possible sources of errors were identified. The results of the evaluation for the four seasonal months indicate that by using the best available auxiliary data, the accuracy achieved in estimating TOA SWR at the instantaneous scale ranges between 0.55 and 17.14 W m⁻² for the bias and 22.21 to 30.64 W m⁻² for the standard deviation of biases (differences are ABI minus CERES). It is believed that the high bias of 17.14 for July is related to the predominantly cloudless sky conditions, when the used ADMs do not perform as well as for cloudy conditions. Full article

The growing demand for ultra-low sulfur fuels has intensified interest in recovering strategic metals from the large volumes of hazardous hydrodesulfurization catalysts that are discarded yearly. This work evaluates a task-specific ionic liquid, tri-n-octylammonium bis(2-,4-,4-trimethylpentyl)-phosphinate [TOA][Cy272], synthesized by the acid–base neutralization of tri-n-octylamine and Cyanex 272. FT-IR spectroscopy confirmed complete proton transfer and the formation of a stable ion pair. Liquid–liquid extraction tests were conducted with synthetic Co–Ni–Mo solutions (0.1–2.5 g/L each), a varying ionic liquid concentration (10–50 vol%), pH (1.5–12.5), and organic/aqueous ratio (1:1). At 35 vol% of ionic liquid and pH 2, the extraction efficiency for Mo reached 94%, with separation factors βMo/Ni = 12 and βMo/Co = 7.5; Co and Ni uptake remained ≤15%. Selectivity decreased at higher metal loadings because of ionic liquid saturation, and an excessive ionic liquid amount (>35%) offered no benefit, owing to viscosity-limited mass transfer. Stripping studies showed that 1 M NH₄OH stripped about 95% Mo, while leaving Co and Ni in the organic phase; conversely, 2 M HCl removed 92–98% of Co and Ni, but <5% Mo. Overall Mo recovery of about 95% was obtained by a two-step extraction/stripping scheme. The results demonstrate that [TOA][Cy272] combines the cation exchange capability of quaternary ammonium ILs with the strong chelating affinity of organophosphinic acids, delivering rapid, selective, and regenerable separation of Mo from mixed-metal leachates and wastewater streams. Full article

Pelvic inflammatory disease (PID) encompasses a broad range of infection-induced inflammatory disorders of the female upper genital tract, commonly caused by ascending sexually transmitted infections. Diagnosis is often challenging because of nonspecific or absent symptoms and the overlap with other pelvic pathologies. While clinical and laboratory assessments are essential, cross-sectional imaging plays a pivotal role, especially in complicated, atypical, or equivocal cases. This review focuses on the typical and atypical imaging features of PID and highlights the crucial roles of computed tomography (CT) and magnetic resonance imaging (MRI) in its diagnostic evaluation. CT is frequently employed in emergency settings because of its widespread availability and ability to detect acute complications such as tubo-ovarian abscesses (TOA), peritonitis, or Fitz-Hugh–Curtis syndrome. However, it is limited by ionizing radiation and suboptimal soft-tissue contrast. MRI provides superior tissue characterization and multiplanar imaging without radiation exposure. When combined with diffusion-weighted imaging (DWI), MRI achieves high diagnostic accuracy, particularly in differentiating PID from other entities such as endometriosis, adnexal tumors, and gastrointestinal or urinary tract diseases. This review also addresses PID in specific clinical contexts, including post-partum infection, post-assisted reproductive technologies (ART), intrauterine device (IUD) use, and chronic or recurrent forms. A comprehensive, multimodal imaging approach integrated with clinical findings is essential for timely diagnosis, effective treatment, and prevention of severe reproductive sequelae. Full article

This study develops a high-accuracy indoor positioning system using ultra-wideband (UWB) technology and the time-of-arrival (TOA) method. The system is built using Arduino Nano microcontrollers and DW1000 UWB chips to measure distances between anchor nodes and a mobile tag. Three positioning algorithms are tested: the triangle centroid algorithm (TCA), inner triangle centroid algorithm (ITCA), and the proposed intersection midpoint algorithm (IMA). Experiments conducted in a 732 × 488 × 220 cm indoor environment show that TCA performs well near the center but suffers from reduced accuracy at the edges. In contrast, IMA maintains stable and accurate positioning across all test points, achieving an average error of 12.87 cm. The system offers low power consumption, fast computation, and high positioning accuracy, making it suitable for real-time indoor applications such as hospital patient tracking and shopping malls where GPS is unavailable or unreliable. Full article

High-frequency (HF) communication is critical for applications such as over-the-horizon positioning and ionospheric detection. However, precise time-delay estimation in complex HF channels faces significant challenges from multipath fading, Doppler shifts, and noise. This paper proposes a Modulation Signal-based Adaptive Time-Delay Estimation (MATE) algorithm, which effectively decouples carrier and modulation signals and integrates phase-locked loop (PLL) and delay-locked loop (DLL) techniques. By leveraging the autocorrelation properties of 8PSK (Eight-Phase Shift Keying) signals, MATE compensates for carrier frequency deviations and mitigates multipath interference. Simulation results based on the Watterson channel model demonstrate that MATE achieves an average time-delay estimation error of approximately 0.01 ms with a standard deviation of approximately 0.01 ms, representing a 94.12% reduction in mean error and a 96.43% reduction in standard deviation compared to the traditional Generalized Cross-Correlation (GCC) method. Validation with actual measurement data further confirms the robustness of MATE against channel variations. MATE offers a high-precision, low-complexity solution for HF time-delay estimation, significantly benefiting applications in HF communication systems. This advancement is particularly valuable for enhancing the accuracy and reliability of time-of-arrival (TOA) detection in HF-based sensor networks and remote sensing systems. Full article

Background/Objectives: Refractory community-acquired pneumonia (r-CAP) has become a thorny issue in clinical practice, especially after the COVID-19 pandemic, even in immunocompetent patients, as conventionally defined. In this study, we aimed to identify the risk factors for immunocompetent patients with r-CAP. Methods: This was a single-center retrospective study. In total, we collected clinical data from 82 patients with r-CAP in whom the first-line antibiotic therapy failed and 82 patients with general CAP (g-CAP) who recovered with first-line antibiotics, matched at a ratio of 1:1, admitted to Beijing Shijitan Hospital, Capital Medical University, from 1 January 2022, to 31 December 2023. The differences between the two groups (clinical characteristics, peripheral blood cell count, lymphocyte subsets, and regular laboratory indicators) were analyzed using paired t, paired Wilcoxon, Chi-square, or Fisher’s exact tests, and univariate and multivariate logistics regression analyses were conducted to identify the independent risk factors. A model for predicting indicators with statistical significance was established and proved with the receiver operating characteristic (ROC) curve. Results: Warm season, a history of chronic obstructive pulmonary disease, longer time from onset to admission (T_O-A), higher percentages of CD4⁺ T, CD8⁺ T, and double-negative T (DNT) lymphocytes, as well as higher levels of C-reactive protein (CRP), low-density lipoprotein cholesterin (LDL-C), serum sodium ion (Na⁺), and free-calcium ion (FCa²⁺) were regarded as independent risk factors, while T lymphocyte percentage (T%) and total cholesterol (TC) were identified as protective factors. The combined multivariate model using all the above factors proved to be sensitive and specific (AUC = 0.8711, p < 0.0001, R² = 0.4235), and thus better than the respective univariate models. Conclusions: Increased CD4⁺ T%Lym, CD8⁺ T%Lym, and DNT%Lym, warm season, a history of COPD, longer T_O-A, and increased levers of CRP, LDL-C, Na⁺, and FCa²⁺ potentially cause CAP to be refractory, while the T lymphocyte count, namely, the overall cellular immunity, was impaired in r-CAP patients, and increased TC levels could be beneficial to pneumonia recovery. Full article

The accurate and reliable positioning of unmanned aerial vehicles (UAVs) in urban environments is crucial for urban air mobility (UAM) application, such as logistics, surveillance, and disaster management. However, global navigation satellite systems (GNSSs) often fail in densely populated areas due to signal reflections (multipath propagation) and obstructions non-line-of-sight (NLOS), causing significant positioning errors. To address this, we propose a machine learning (ML) framework that integrates 5G position reference signals (PRSs) to correct UAV position estimates. A dataset was generated using MATLAB’s UAV simulation environment, including estimated coordinates derived from 5G time of arrival (TOA) measurements and corresponding actual positions (ground truth). This dataset was used to train a fully connected feedforward neural network (FNN), which improves the positioning accuracy by learning patterns between predicted and actual coordinates. The model achieved significant accuracy improvements, with a mean absolute error (MAE) of 1.3 m in line-of-sight (LOS) conditions and 1.7 m in NLOS conditions, and a root mean squared error (RMSE) of approximately 2.3 m. The proposed framework enables real-time correction capabilities for dynamic UAV tracking systems, highlighting the potential of combining 5G positioning data with deep learning to enhance UAV navigation in urban settings. This study addresses the limitations of traditional GNSS-based methods in dense urban environments and offers a robust solution for future UAV advancements. Full article