Search Results (297)

As global demand for rare earth elements (REEs) increases, maintaining the production and supply chain is critical. Technologies capable of being used in the field and in situ in the subsurface for rapid REE detection and quantification facilitates the efficient mining of known resources and exploration of new and unconventional resources. Laser-induced breakdown spectroscopy (LIBS) is a promising technique for rapid elemental analysis both in the laboratory and in the field. Multiple articles have been published evaluating LIBS for detection and quantification of REEs; however, REEs in their natural deposits have not been adequately studied. In this work, detection and quantification of two REEs, La and Nd, have been studied in both synthetic and natural mineral matrices at concentrations relevant to REE extraction. Measurements were performed on REE-containing rock and coal samples (natural and synthetic) utilizing different LIBS instruments and techniques, specifically a commercial benchtop instrument, a custom benchtop instrument (single- and double-pulse modes), and a custom LIBS probe currently being developed for in situ, subsurface, borehole wall detection and quantification of REEs. Plasma expansion, emission intensity, detection limits, and double-pulse signal enhancement were studied. The limits of detection (LOD) were found to be 10/14 ppm for La and 15/25 ppm for Nd in simulated coal/rock matrices in single-pulse mode. Signal enhancement of 3.5 to 6-fold was obtained with double-pulse mode as compared to single-pulse operation. Full article

The substantial volume of data acquired through remote-detection acoustic logging poses a remarkable challenge because of the limited real-time upload speed of the cable, which severely impedes its further application. To address this issue, a two-stage data compression method that was implemented at the acquisition node was proposed in this study. This approach includes a field programmable gate array (FPGA)-based hardware system and a two-stage downhole data compression algorithm combining wavelet transform and adaptive differential pulse-code modulation paired with ground decompression software. Finally, the proposed compression method was evaluated using actual logging data. The test results revealed that the overall compression rate of the two-stage compression method was 25.1%. The reconstructed waveforms highly retained the overall shape of the original waveforms, and the severe relative distortion of individual data points did not affect the extraction of the sliding longitudinal, sliding transverse and reflected waveforms. The FPGA compressed 2048 16-bit waveforms in approximately 100 μs with low resource utilization and workload. It considerably outperformed DSP-based pre-transmission compression. Herein, the data compression method at the acquisition node helped in reducing the workload on the master control node and increasing the effective speed of the cable transmission up to 400%, thereby enhancing the remote-detection acoustic logging. Full article

Enhancing the resilience of renewable energy systems in ultra-weak grids is crucial for promoting sustainable energy adoption and ensuring a reliable power supply during disturbances. Ultra-weak grids characterized by a very low Short-Circuit Ratio, less than 2, and high grid impedance significantly impair voltage and frequency stability, imposing challenging conditions for Inverter-Based Resources. To address these challenges, this paper considers a 110 KVA, three-phase, two-level Voltage Source Converter, interfacing a 700 V DC link to a 415 V AC ultra-weak grid. X/R = 1 is controlled using Sinusoidal Pulse Width Modulation, where the Grid-Connected Converter operates in Grid-Forming Mode to maintain voltage and frequency stability under a steady state. During symmetrical and asymmetrical faults, the converter transitions to Grid-Following mode with current control to safely limit fault currents and protect the system integrity. After fault clearance, the system seamlessly reverts to Grid-Forming Mode to resume voltage regulation. This paper proposes an improved control strategy that integrates voltage feedforward reactive power support and virtual capacitor-based virtual inertia using Active Disturbance Rejection Control, a robust, model-independent controller, which rapidly rejects disturbances by regulating d and q-axes currents. To test the practicality of the proposed system, real-time implementation is carried out using the OPAL-RT OP4610 platform, and the results are experimentally validated. The results demonstrate improved fault current limitation and enhanced DC link voltage stability compared to a conventional PI controller, validating the system’s robust Fault Ride-Through performance under ultra-weak grid conditions. Full article

To ensure the on-orbit safety of crewed spacecraft and avoid the threat of constellations such as Starlink to manned spacecraft, the industry has started to research equipping phased array radars for situational awareness of collision threat. In order to enhance the resource allocation capability of the space station’s protection radar system, this paper proposes a task scheduling method based on time shifting constraints and pulse interleaving. The time shifting constraint is designed to minimize the deviation between the actual execution and the desired execution time of the task, and it is negatively correlated with the threat degree of the target. Pulse interleaving is intended to utilize the idle time between the transmitted pulse and the received pulse of a task to perform other tasks, thereby improving the utilization of radar resources. Through computer simulation under typical parameters, our proposed method reduces the average time shifting ratio by about 60% compared to traditional task scheduling methods, and the scheduling success ratio is also higher than that of traditional scheduling methods. This demonstrates the effectiveness of the proposed method in enhancing scheduling efficiency and overall system performance. Full article

This paper presents a hybrid fast charging strategy for static wireless power transfer (WPT) systems that synergistically combines pulsed current and multi-stage current (MCM) modes to enable rapid yet battery-health-conscious electric vehicle (EV) charging, thereby promoting sustainable transportation. The proposed approach employs a memetic algorithm (MA) to dynamically optimize the charging parameters, achieving an optimal balance between speed and battery longevity while maintaining 90.78% system efficiency at the SAE J2954-standard 85 kHz operating frequency. A neural-network-based state of charge (SOC) estimator provides accurate real-time monitoring, complemented by MA-tuned PI control for enhanced resonance stability and adaptive pulsed current–MCM profiles for the optimal energy transfer. Simulations and experimental validation demonstrate faster charging compared to that using the conventional constant current–constant voltage (CC-CV) methods while effectively preserving the battery’s state of health (SOH)—a critical advantage that reduces the environmental impact of frequent battery replacements and minimizes the carbon footprint associated with raw material extraction and battery manufacturing. By addressing both the technical challenges of high-power WPT systems and the ecological imperative of battery preservation, this research bridges the gap between fast charging requirements and sustainable EV adoption, offering a practical solution that aligns with global decarbonization goals through optimized resource utilization and an extended battery service life. Full article

The increasing demand for sustainable protein sources has intensified interest in improving the processing efficiency of traditional proteins and developing novel alternatives, particularly those derived from plants and algae. Among various processing technologies, pH shifting has attracted attention due to its simplicity, low cost, and capacity to effectively alter protein structure and functionality. However, employing pH shifting alone requires extremely acidic or alkaline conditions, which can lead to protein denaturation and the generation of undesirable by-products. To address these limitations, this review explores the integration of pH shifting with physical processing techniques such as ultrasound, high-pressure processing, pulsed electric fields, and thermal treatments. Moreover, this review highlights the effects of these combined treatments on protein conformational transitions and the resulting improvements in functional properties such as solubility, emulsification, foaming capacity, and thermal stability. Importantly, they reduce reliance on extreme chemical conditions, providing greater sustainability in industrial applications, particularly in food product development where milder processing conditions help preserve nutritional quality and functional properties. In that sense, this combined treatment approach provides a promising and eco-efficient protein modification strategy, and bridges technological innovation with sustainable resource utilization. Full article

The Advanced Topographic Laser Altimeter System (ATLAS) on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a photon-counting detection mode with a 532 nm laser to obtain high-precision Earth surface elevation data and offers a new remote sensing method for nearshore bathymetry. The key issues in using ATLAS ATL03 data for bathymetry are achieving automatic and accurate extraction of signal photons in different water environments. Especially for areas with sharply fluctuating topography, the interaction of various impacts, such as topographic fluctuations, sea waves, and laser pulse direction, can result in a sharp change in photon density and distribution at the seafloor, which can cause the signal photon detection at the seafloor to be misinterpreted or omitted during analysis. Therefore, an improved size and direction adaptive filtering (ISDAF) method was proposed for nearshore bathymetry using ATLAS ATL03 data. This method can accurately distinguish between the original photons located above the sea surface, on the sea surface, and the seafloor. The size and direction of the elliptical density filter kernel automatically adapt to the sharp fluctuations in topography and changes in water depth, ensuring precise extraction of signal photons from both the sea surface and the seafloor. To evaluate the precision and reliability of the ISDAF, ATLAS ATL03 data from different water environments and seafloor terrains were used to perform bathymetric experiments. Airborne LiDAR bathymetry (ALB) data were also used to validate the bathymetric accuracy and reliability. The experimental findings show that the ISDAF consistently exhibits effectiveness in detecting and retrieving signal photons, regardless of whether the seafloor terrain is stable or dynamic. After applying refraction correction, the high accuracy of bathymetry was evidenced by a strong coefficient of determination (R²) and a low root mean square error (RMSE) between the ICESat-2 bathymetry data and ALB data. This research offers a promising approach to advancing remote sensing technologies for precise nearshore bathymetric mapping, with implications for coastal monitoring, marine ecology, and resource management. Full article

Background/Objectives: Accurate and timely triage is essential for optimizing clinical outcomes and resource allocation in emergency departments (EDs). The Peripheral Perfusion Index (PPI), a non-invasive and objective parameter derived from pulse oximetry, may offer added value in early risk stratification. This study aimed to analyze the correlation between the PPI measured at triage and at Emergency Severity Index (ESI) levels, as well as to determine if the PPI may function as a predictive tool to facilitate early risk identification before patient disposition. Methods: In this prospective cross-sectional study, adult ambulatory patients presenting to a tertiary care ED were enrolled. At triage, PPI and standard vital signs were recorded, and patients were classified using the five-level ESI system. The diagnostic performance of PPI and ESI in predicting ED discharge was assessed using receiver operating characteristic (ROC) curve analysis, with comparative evaluation performed via DeLong’s test. Results: Lower PPI values were consistently associated with higher ESI acuity levels and more intensive care requirements. Patients who were discharged had significantly higher median PPI values (4.0) compared to those admitted to wards (2.1) or intensive care units (1.9). PPI also distinguished survivors from non-survivors (median PPI: 3.60 vs. 1.15). ROC analysis showed that the PPI demonstrated a good discriminative capacity for forecasting ED discharge, equal to the efficacy of ESI (AUC: 0.926 vs. 0.903; p < 0.001). Conclusions: The PPI could improve post-triage risk classification and enhance current triage techniques like ESI, especially in cases of unclear or borderline presentations, but further validation in prospective trials is required. Full article

To investigate the species composition, reproductive dynamics, and hydrological drivers of fish resources in the early stage in the Guiping section of the Xunjiang River, we conducted a two-year survey (2022–2023) downstream of the Datengxia Dam. A total of 22,464 fish eggs and larvae were collected, representing 6 orders, 17 families, and 67 species, with Cyprinidae (58.2%) as the dominant family. Dominant species included Squaliobarbus curriculus, Gobiidae, Hemiculter leucisculus, and Culter, exhibiting significant interannual variation in abundance. The breeding season peaked from May to September, accounting for 94.6% of annual recruitment. Hydrological conditions strongly influenced reproductive output: the multiple flood pulse periods in 2022 (peak discharge: 29,000 m³/s) yielded 34.997 billion eggs and larvae, whereas reduced flows in 2023 (peak discharge: 12,200 m³/s) led to a 75.4% decline (8.620 billion). Redundancy analysis (RDA) revealed that discharge, water temperature, natural hydrological data, and dissolved oxygen were the primary environmental drivers, explaining 46.11% of variability in larval abundance (p < 0.001). Notably, the proportion of important economic fish, “four major Chinese carps”, plummeted from 4.9% (2022) to less than 0.1% (2023), indicating spawning ground function degradation. Our results demonstrate that flood pulses are essential for sustaining fish recruitment, particularly for pelagic spawning riverine fish like the four major Chinese carps. Their proportion plummeted to less than 0.1% in 2023, highlighting the urgent need for eco-hydrological management in the Xunjiang River. Full article

The effective use of biowaste resources becomes crucial for the development of bioprocessing alternatives to current oil- and chemical-based value chains. Targeting the development of multi-product biorefinery approaches benefits the viability and profitability of these process schemes. Certain oleaginous microorganisms, such as oleaginous red yeast, can co-produce industrially relevant bio-based products. This work aims to explore the use of industrial and urban waste as cost-effective feedstock for producing microbial oil and carotenoids using Rhodosporidium toruloides. The soluble fraction, resulting from homogenization, crushing, and centrifugation of discarded vegetable waste, was used as substrate under a pulse-feeding strategy with a concentrated enzymatic hydrolysate from municipal forestry residue obtained after steam explosion pretreatment (190 °C, 10 min, and 40 mg H₂SO₄/g residue). Additionally, the initial nutrient content was investigated to enhance process productivity values. The promising results of these cultivation strategies yield a final cell concentration of 36.4–55.5 g/L dry cell weight (DCW), with an intracellular lipid content of up to 42–45% (w/w) and 665–736 µg/g DCW of carotenoids. These results demonstrate the potential for optimizing the use of waste resources to provide effective alternative uses to current biowaste management practices, also contributing to the market of industrially relevant products with lower environmental impacts. Full article

The rapid growth of inverter-based resources (IBRs) has created a need for new islanding detection methodologies to determine whether an IBR has been disconnected from the transmission grid in some manner (islanded) or remains connected to the transmission grid (grid-connected). Active islanding detection methods inject a signal into the power system to achieve detection. Existing schemes frequently limit consideration to a single node system with one IBR. Schemes tested on multiple IBRs often see interference, with the signals from one IBR disturbing the others, or require intricate communication. Further, several methods destabilize an islanded grid to detect it, preventing a prospective microgrid from remaining in operation while islanded. This work develops an active islanding detection scheme using Pulse Compression Probing (PCP) that is microgrid-compatible and can be used with multiple IBRs without requirement for communication. This active islanding detection scheme can be implemented on existing inverter switching sequences and has a detection time of 167–223 ms, well within the detection time specified by existing standards. The method is verified via electromagnetic transient (EMT) simulation on a modified version of a 34-bus test system. Full article

The rapid development of unmanned aerial vehicles (UAVs) has had a significant impact on the growth of the economic, industrial, and social welfare of society. The possibility of reaching places that are difficult and dangerous for humans to access with minimal use of third-party resources increases the efficiency and quality of maintenance of construction structures, agriculture, and exploration, which are carried out with the help of drones with a predetermined trajectory. The widespread use of UAVs has caused problems with the control of the drones’ correctness following a given route, which leads to emergencies and accidents. Therefore, UAV monitoring with video cameras is of great importance. In this paper, we propose a Yolov12 architecture with positive–negative pulse-based optimization algorithms to solve the problem of drone detection on video data. Self-attention-based mechanisms in transformer neural networks (NNs) improved the quality of drone detection on video. The developed algorithms for training NN architectures improved the accuracy of drone detection by achieving the global extremum of the loss function in fewer epochs using positive–negative pulse-based optimization algorithms. The proposed approach improved object detection accuracy by 2.8 percentage points compared to known state-of-the-art analogs. Full article

This study evaluates the feasibility of integrating pulsed electric field (PEF) technology with heat recovery for fruit juice pasteurization, comparing it to conventional high-temperature short-time (HTST) pasteurization. Three preheating temperature conditions (35 °C, 45 °C, and 55 °C) and varying heat recovery efficiencies have been assessed to analyze energy consumption, economic feasibility, and environmental impact. The results indicate that, while PEF pasteurization requires a higher initial investment, it improves energy efficiency, leading to significant reductions in utility costs. Across the tested configurations, PEF technology achieved reductions in electricity consumption by up to 20%, fuel gas usage by over 60%, greenhouse gas emissions by approximately 30%, and water consumption by 25%, compared to HTST. The optimal configuration of the PEF process, featuring a 35% waste heat recovery efficiency and a pre-heating temperature of 55 °C, has been identified as the most energy-efficient and sustainable solution, effectively reducing both water consumption and CO₂ emissions. A life cycle assessment has confirmed these environmental benefits, demonstrating reductions in global warming potential, fossil fuel consumption, and other impact categories. This study suggests that PEF technology can significantly contribute to more sustainable food processing by reducing environmental impacts, optimizing resource usage, and enhancing energy efficiency. Full article

Cemented paste backfill (CPB) plays an important role in sustainable mining by providing structural support and reducing surface subsidence. While traditional destructive testing methods such as unconfined compressive strength (UCS) tests offer valuable understanding of material strength, they require a lot of resources, are time-consuming, and environmentally unfriendly. However, non-destructive testing (NDT) techniques such as ultrasonic pulse velocity (UPV), electrical resistivity (ER), and acoustic emission (AE) provide sustainable alternatives by preserving sample integrity, minimizing waste, and enabling real-time monitoring. This study systematically reviews and quantitatively compares the effectiveness of UPV, ER, and AE in predicting the strength of CPB. Meta-analysis of 30 peer-reviewed studies reveals that UPV and AE provide the most consistent and reliable correlations with UCS, with R² values of 0.895 and 0.896, respectively, while ER shows more variability due to its sensitivity to environmental factors. Additionally, a synthetic model combining UPV, AE and ER demonstrates improved accuracy in predicting strength. This hybrid approach enhances predictions of material performance while supporting sustainability in mining and construction. Our research advocates for better testing practices and presents a promising direction for future infrastructure projects, where real-time, non-invasive monitoring can enhance material performance evaluation and optimize resource use. Full article

Efficient real-time isotope identification is a critical challenge in nuclear spectroscopy, with important applications such as radiation monitoring, nuclear waste management, and medical imaging. This work presents a novel approach for isotope classification using a System-on-Chip FPGA, integrating hardware-accelerated principal component analysis (PCA) for feature extraction and a software-based random forest classifier. The system leverages the FPGA’s parallel processing capabilities to implement PCA, reducing the dimensionality of digitized nuclear signals and optimizing computational efficiency. A key feature of the design is its ability to perform real-time classification without storing ADC samples, directly processing nuclear pulse data as it is acquired. The extracted features are classified by a random forest model running on the embedded microprocessor. PCA quantization is applied to minimize power consumption and resource usage without compromising accuracy. The experimental validation was conducted using datasets from high-resolution pulse-shape digitization, including closely matched isotope pairs (¹²C/¹³C, ³⁶Ar/⁴⁰Ar, and ⁸⁰Kr/⁸⁴Kr). The results demonstrate that the proposed SoC FPGA system significantly outperforms conventional software-only implementations, reducing latency while maintaining classification accuracy above 98%. This study provides a scalable, precise, and energy-efficient solution for real-time isotope identification. Full article