Search Results (154)

The citrate–nitrate method was employed to synthesize the magnesium chromite (MgCr₂O₄) spinel, followed by calcination at 700 °C for 3 h. The synthesized compound was analyzed using techniques including powder XRD, SEM-EDAX, FTIR, UV-DRS, and LCR Meter. The structural analysis was conducted using an X-ray diffractometer, which revealed the formation of the cubic crystal symmetry of the sample with the corresponding Fd-3 m space group. The average crystallite size of the sample was calculated around 15.38 nm. Using tetrahedral and octahedral positions, the lattice vibrations of the associated chemical bonds were identified using Fourier transform infrared (FTIR) spectroscopy. SEM (scanning electron microscopy) micrographs showed that the spherical nature of the particles and the constituent particles were between 10 and 40 nm in size. The optical bandgap value was evaluated using Tauc’s plot. Pellets of the powdered sample were prepared for determining the dielectric aspects, such as the dielectric constant (ε′) and tangent loss (tanδ), in the frequency range of 10 Hz–8 MHz at room temperature. The charge transport mechanism was explored from the complex impedance spectroscopy study. The obtained results indicate that magnesium chromite may be a potential candidate in the fabrication of sensors, micro-electronic devices, etc. Full article

Accurate and reliable characterization of current transformer (CT) performance is essential for maintaining grid stability and power quality in modern electrical networks. CT measurements are key to effective monitoring of harmonic distortions, supporting regulatory compliance and ensuring the safe operation of the grid. This paper addresses a method for the characterization of CTs across an extended frequency range from 50 Hz up to 150 kHz, driven by increasing power quality issues introduced by renewable energy installations and non-linear loads. Traditional CT calibration approaches involve measurement setups that offer ppm-level uncertainty but are complex to operate and limited in practical frequency range. To simplify and expand calibration capabilities, a calibration system employing a sampling ammeter (power analyzer) was developed, enabling the direct measurement of CT secondary currents of an unknown CT and a reference CT without any further auxiliary equipment. The resulting expanded magnitude ratio uncertainties for the wideband CT calibration system are 10 ppm ($k=2$) up to 10 kHz and less than 120 ppm from 10 kHz to 150 kHz; these uncertainties do not include the uncertainty of the reference CT. Additionally, the operational conditions and setup design choices, such as instrument warm-up duration, grounding methods, measurement shunt selection, and cable type, were evaluated for their impact on measurement uncertainty and repeatability. The results highlight the significance of minimizing parasitic impedances at higher frequencies and maintaining consistent testing conditions. The developed calibration setup provides a robust foundation for future standardization efforts and practical guidance to characterize CT performance in the increasingly important supraharmonic frequency range. Full article

This paper proposes a method for designing a dual-band low-noise amplifier (DB-LNA) using a new improved complex impedance dual-band transformer (IDBT). This complex IDBT is composed of parallel-coupled lines and two sections of series microstrip lines. The parallel-coupled lines are used to complete the transformation from complex impedances at two different frequencies to a pair of conjugate complex impedances, meanwhile eliminating the need for DC blocking capacitors. The transformation to real impedances is achieved by series microstrip lines at dual frequency points. A single-stage DB-LNA was designed using the BFP840ESD transistor in combination with the proposed IDBT. The fabrication and testing of the Printed Circuit Board (PCB) were then completed. The measured results of the proposed 2.4/5.5 GHz DB-LNA show an S21 parameter of 20.3/14.7 dB, an S11 of −29.8/−20.3 dB, an S22 of −15.2/−16.4 dB, and a noise figure (NF) of 1.6/1.6 dB. The whole DB-LNA has a simple structure, low cost, and excellent performance and is easy to tune. Full article

This work presents a comprehensive evaluation of corrosion progression in DH36 naval steel through the integration of electrochemical impedance spectroscopy (EIS), weight loss, scanning electron microscopy (SEM), and advanced magnetic non-destructive techniques under artificial seawater (ASW, ASTM D1141) and natural marine conditions. Quantitative correlations are established between corrosion layer growth, electrochemical parameters, and magnetic permeability, demonstrating the magnetic sensor’s capacity for the real-time, non-invasive assessment of marine steel degradation. Laboratory exposures reveal a rapid initial corrosion phase with the formation of lepidocrocite and goethite, followed by the densification of the corrosion product layer and a pronounced decline in corrosion rate, ultimately governed by diffusion-controlled kinetics. Notably, changes in magnetic permeability closely track both the thickening of non-magnetic corrosion products and microstructural deterioration, with declining μmax and increased hysteresis widths (FWHM) sensitively indicating evolving surface conditions. A direct comparison with in situ marine immersion at Rafina confirms that the evolution of corrosion morphology and the corresponding magnetic response are further modulated by biofilm development, which exacerbates the attenuation of measured surface permeability and introduces greater variability linked to biological activity. These findings underscore the robustness and diagnostic potential of magnetic non-destructive sensors for the predictive, condition-based monitoring of naval steels, bridging laboratory-controlled observations and complex real-world environments with high quantitative fidelity to corrosion kinetics, phase evolution, and microstructural transformations, thus guiding the strategic deployment of protection and maintenance regimens for naval fleet integrity. Full article

Lubricant condition analysis is a valuable diagnostic tool for assessing engine performance and ensuring the reliable operation of diesel engines. While traditional diagnostic techniques—such as Fourier transform infrared spectroscopy (FTIR)—are constrained by slow response times, high costs, and the need for specialized personnel. In contrast, dielectric spectroscopy, impedance analysis, and soft computing offer real-time, non-destructive, and cost-effective alternatives. This review examines recent advances in integrating these techniques to predict lubricant properties, evaluate wear conditions, and optimize maintenance scheduling. In particular, dielectric and impedance spectroscopies offer insights into electrical properties linked to oil degradation, such as changes in viscosity and the presence of wear particles. When combined with soft computing algorithms, these methods enhance data analysis, reduce reliance on expert interpretation, and improve predictive accuracy. The review also addresses challenges—including complex data interpretation, limited sample sizes, and the necessity for robust models to manage variability in real-world operations. Future research directions emphasize miniaturization, expanding the range of detectable contaminants, and incorporating multi-modal artificial intelligence to further bolster system robustness. Collectively, these innovations signal a shift from reactive to predictive maintenance strategies, with the potential to reduce costs, minimize downtime, and enhance overall engine reliability. This comprehensive review provides valuable insights for researchers, engineers, and maintenance professionals dedicated to advancing diesel engine lubricant monitoring. Full article

The central and deep reservoirs of the Wushi Sag in the Beibu Gulf Basin, China, are characterized by structurally complex settings, strong heterogeneity, multiple controlling factors for physical properties of reservoirs, rapid lateral variations in reservoir thickness and petrophysical properties, and limited seismic resolution. To address these challenges, this study integrates the INPEFA inflection point technique and Morlet wavelet transform to delineate system tracts and construct a High-Frequency Stratigraphic Framework (HFSF). Sedimentary facies are identified through the integration of core descriptions and seismic data, enabling the mapping of facies distributions. The vertical constraints provided by the stratigraphic framework, combined with the lateral control from facies distribution, which, based on identification with logging data and geological data, support the construction of a geologically consistent low-frequency initial model. Subsequently, geostatistical seismic inversion is performed to derive acoustic impedance and lithological distributions within the central and deep reservoirs. Compared with the traditional methods, the accuracy of the inversion results of this method is 8% higher resolution than that of the conventional methods, with improved vertical resolution to 3 m, and enhances the lateral continuity matched with the sedimentary facies structure. This integrated workflow provides a robust basis for predicting the spatial distribution of sandstone reservoirs in the Wushi Sag’s deeper stratigraphic intervals. Full article

Global context information is essential for semantic segmentation of remote sensing (RS) images. Due to their remarkable capability to capture global context information and model long-range dependencies, vision transformers have demonstrated great performance on semantic segmentation. However, the high computational complexity of vision transformers impedes their broad application in resource-constrained environments for RS image segmentation. To address this challenge, we propose multi-faceted adaptive token pruning (MATP) to reduce computational cost while maintaining relatively high accuracy. MATP is designed to prune well-learned tokens which do not have a close relation to other tokens. To quantify these two metrics, MATP employs multi-faceted scores: entropy, to evaluate the learning progression of tokens; and attention weight, to assess token correlations. Specially, MATP utilizes adaptive criteria for each score that are automatically adjusted based on specific input features. A token is pruned only when both criteria are satisfied. Overall, MATP facilitates the utilization of vision transformers in resource-constrained environments. Experiments conducted on three widely used datasets reveal that MATP reduces the computation cost about 67–70% with about 3–6% accuracy degradation, achieving a superior trade-off between accuracy and computational cost compared to the state of the art. Full article

The escalating threat of infectious diseases necessitates the development of diagnostic technologies that are not only rapid and sensitive but also deployable at the point of care. Electrochemical impedance spectroscopy (EIS) has emerged as a leading technique for the label-free detection of pathogens, offering a unique combination of sensitivity, non-invasiveness, and adaptability. This review provides a comprehensive overview of the design and application of EIS-based biosensors tailored for pathogen detection, focusing on critical components such as biorecognition elements, electrode materials, nanomaterial integration, and surface immobilization strategies. Special emphasis is placed on the mechanisms of signal generation under Faradaic and non-Faradaic modes and how these underpin performance characteristics such as the limit of detection, specificity, and response time. The application spectrum spans bacterial, viral, fungal, and parasitic pathogens, with case studies highlighting detection in complex matrices such as blood, saliva, food, and environmental water. Furthermore, integration with microfluidics and point-of-care systems is explored as a pathway toward real-world deployment. Emerging strategies for multiplexed detection and the utilization of novel nanomaterials underscore the dynamic evolution of the field. Key challenges—including non-specific binding, matrix effects, the inherently low ΔR_ct/decade sensitivity of impedance transduction, and long-term stability—are critically evaluated alongside recent breakthroughs. This synthesis aims to support the future development of robust, scalable, and user-friendly EIS-based pathogen biosensors with the potential to transform diagnostics across healthcare, food safety, and environmental monitoring. Full article

Machine learning (ML) has emerged as a transformative force in smart building management due to its ability to significantly enhance energy efficiency and promote sustainability within the built environment. This review examines the pivotal role of ML in optimizing building operations through the application of predictive analytics and sophisticated automated control systems. It explores the diverse applications of ML techniques in critical areas such as energy forecasting, non-intrusive load monitoring (NILM), and predictive maintenance. A thorough analysis then identifies key challenges that impede widespread adoption, including issues related to data quality, privacy concerns, system integration complexities, and scalability limitations. Conversely, the review highlights promising emerging opportunities in advanced analytics, the seamless integration of renewable energy sources, and the convergence with the Internet of Things (IoT). Illustrative case studies underscore the tangible benefits of ML implementation, demonstrating substantial energy savings ranging from 15% to 40%. Future trends indicate a clear trajectory towards the development of highly autonomous building management systems and the widespread adoption of occupant-centric designs. Full article

This paper presents a co-design approach for a microwave amplifier–phase shifter that integrates an arbitrary termination impedance reflection-type phase shifter as the input matching network of a microwave transistor. Since the proposed reflection-type phase shifter input matching network is capable of transforming both real and/or complex impedances to a system impedance of 50 Ω, the co-design approach can directly match the optimum source impedance of the microwave transistor to 50 Ω through a reflection-type phase shifter input matching network. To validate the proposed method, prototypes of microwave amplifier–phase shifters with different input matching networks configurations are designed, fabricated, and measured with a center frequency of 2.45 GHz. The experimental results demonstrate that the proposed co-design microwave amplifier–phase shifter achieves improved electrical performances compared to the conventional approach, where a 50-to-50 Ω termination impedance phase shifter is cascaded with a 50-to-50 Ω termination impedance conventional microwave amplifier. Measurement results demonstrate that the gains of a standalone conventional microwave amplifier, a cascaded phase shifter with a conventional microwave amplifier, and the proposed co-design microwave amplifier–phase shifter are 14.13 dB, 13.28 dB, and 13.74 dB, while the 1 dB compression points are 25.72 dBm, 24.77 dBm, and 25.26 dBm, respectively. Within the 200 MHz bandwidth, the proposed co-design microwave amplifier–phase shifter exhibits a maximum phase shift range of 185.62° and a phase deviation error of ±4.3°. The circuit size of the co-designed microwave amplifier–phase shifter is 38.5% smaller than the conventional cascaded phase shifter with a conventional microwave amplifier. Full article

Escalating environmental challenges severely impede global sustainable development, prompting countries worldwide to innovate environmental governance approaches. As the world’s largest developing country, China’s paradigm shifts in environmental governance from “pollution control” to “ecological conservation” embody many inherent complexities. To investigate the evolution and underlying logic of such paradigm shifts, this study introduces a nested asynchrony framework. Employing a mixed-methods approach that integrates qualitative content analysis, Social Network Analysis, and machine learning, this study analyzes China’s environmental planning documents since the 11th Five-Year Plan to clarify the process of the paradigm shifts and their driving mechanisms. The principal conclusions derived from this study are as follows: (1) Environmental planning is uniquely valued as an analytical lens for identifying paradigm shifts in environmental governance. (2) The paradigm shifts in environmental governance are temporally distinct, wherein transformations in value norms precede structural reforms, while shifts in action logic and disciplinary foundations exhibit path-dependent inertia. (3) Inconsistencies within the planning authority framework spanning central and local governments impede the effective allocation and implementation of resources. This study reconstructs the transformation pathway of environmental governance paradigms, validates computational methods in policy analysis, and presents a longitudinal framework for tracking governance evolution. Applicable to other countries or sectors undergoing similar sustainable development transitions, the framework can provide broader utility. Full article

The development of vaccines against viral infections has advanced rapidly over the past century, propelled by innovations in laboratory and molecular technologies. These advances have expanded the range of vaccine platforms beyond live-attenuated and inactivated vaccines to include recombinant platforms, such as subunit proteins and virus-like particles (VLPs), and more recently, mRNA-based vaccines, while also enhancing methods for evaluating vaccine performance. Despite these innovations, a persistent challenge remains: the inherent complexity and heterogeneity of immune responses continue to impede efforts to achieve consistently effective and durable protection across diverse populations. Single-cell technologies have emerged as transformative tools for dissecting this immune heterogeneity, providing comprehensive and granular insights into cellular phenotypes, functional states, and dynamic host–pathogen interactions. In this review, we examine how single-cell epigenomic, transcriptomic, proteomic, and multi-omics approaches are being integrated across all stages of vaccine development—from infection-informed discovery to guide vaccine design, to high-resolution evaluation of efficacy, and refinement of cell lines for manufacturing. Through representative studies, we highlight how insights from these technologies contribute to the rational design of more effective vaccines and support the development of personalized vaccination strategies. Full article

Cancer research faces significant biological, technological, and systemic limitations that hinder the development of effective therapies and improved patient outcomes. Traditional preclinical models, such as 2D and 3D cell cultures, murine xenografts, and organoids, often fail to reflect the complexity of human tumor architecture, microenvironment, and immune interactions. This discrepancy results in promising laboratory findings not always translating effectively into clinical success. A core obstacle is tumor heterogeneity, characterized by diverse genetic, epigenetic, and phenotypic variations within tumors, which complicates treatment strategies and contributes to drug resistance. Hereditary malignancies and cancer stem cells contribute strongly to generating this complex panorama. Current early detection technologies lack sufficient sensitivity and specificity, impeding timely diagnosis. The tumor microenvironment, with its intricate interactions and resistance-promoting factors, further promotes treatment failure. Additionally, we only partially understand the biological processes driving metastasis, limiting therapeutic advances. Overcoming these barriers involves not only the use of new methodological approaches and advanced technologies, but also requires a cultural effort by researchers. Many cancer studies are still essentially observational. While acknowledging their significance, it is crucial to recognize the shift from deterministic to indeterministic paradigms in biomedicine over the past two to three decades, a transition facilitated by systems biology. It has opened the doors of deep metabolism where the functional processes that control and regulate cancer progression operate. Beyond biological barriers, systemic challenges include limited funding, regulatory complexities, and disparities in cancer care access across different populations. These socio-economic factors exacerbate research stagnation and hinder the translation of scientific innovations into clinical practice. Overcoming these obstacles requires multidisciplinary collaborations, advanced modeling techniques that better emulate human cancer, and innovative technologies for early detection and targeted therapy. Strategic policy initiatives must address systemic barriers, promoting health equity and sustainable research funding. While the complexity of cancer biology and systemic challenges are formidable, ongoing scientific progress and collaborative efforts inspire hope for breakthroughs that can transform cancer diagnosis, treatment, and survival outcomes worldwide. Full article

4H-silicon carbide (4H-SiC) is a cornerstone for next-generation optoelectronic and power devices owing to its unparalleled thermal, electrical, and optical properties. However, its chemical inertness and low dopant diffusivity for most dopants have historically impeded effective doping. This study unveils a transformative laser-assisted boron doping technique for n-type 4H-SiC, employing a pulsed Nd:YAG laser (λ = 1064 nm) with a liquid-phase boron precursor. By leveraging a heat-transfer model to optimize laser process parameters, we achieved dopant incorporation while preserving the crystalline integrity of the substrate. A novel optical characterization framework was developed to probe laser-induced alterations in the optical constants—refraction index ($\mathrm{n}$) and attenuation index ($\mathrm{k}$)—across the MIDIR spectrum (λ = 3–5 µm). The optical properties pre- and post-laser doping were measured using Fourier-transform infrared spectrometry, and the corresponding complex refraction indices were extracted by solving a coupled system of nonlinear equations derived from single- and multi-layer absorption models. These models accounted for the angular dependence in the incident beam, enabling a more accurate determination of $\mathrm{n}$ and $\mathrm{k}$ values than conventional normal-incidence methods. Our findings indicate the formation of a boron-acceptor energy level at 0.29 eV above the 4H-SiC valence band, which corresponds to λ = 4.3 µm. This impurity level modulated the optical response of 4H-SiC, revealing a reduction in the refraction index from 2.857 (as-received) to 2.485 (doped) at λ = 4.3 µm. Structural characterization using Raman spectroscopy confirmed the retention of crystalline integrity post-doping, while secondary ion mass spectrometry exhibited a peak boron concentration of 1.29 × 10¹⁹ cm⁻³ and a junction depth of 450 nm. The laser-fabricated p–n junction diode demonstrated a reverse-breakdown voltage of 1668 V. These results validate the efficacy of laser doping in enabling MIDIR tunability through optical modulation and functional device fabrication in 4H-SiC. The absorption models and doping methodology together offer a comprehensive platform for paving the way for transformative advances in optoelectronics and infrared materials engineering. Full article

Non-destructive testing (NDT) and non-destructive evaluation (NDE) are essential tools for ensuring the structural integrity, safety, and reliability of critical systems across the aerospace, civil infrastructure, energy, and advanced manufacturing sectors. As engineered materials evolve into increasingly complex architectures such as fiber-reinforced polymers, fiber–metal laminates, sandwich composites, and functionally graded materials, traditional NDT techniques face growing limitations in sensitivity, adaptability, and diagnostic reliability. This comprehensive review presents a multi-dimensional classification of NDT/NDE methods, structured by physical principles, functional objectives, and application domains. Special attention is given to hybrid and multi-material systems, which exhibit anisotropic behavior, interfacial complexity, and heterogeneous defect mechanisms that challenge conventional inspection. Alongside established techniques like ultrasonic testing, radiography, infrared thermography, and acoustic emission, the review explores emerging modalities such as capacitive sensing, electromechanical impedance, and AI-enhanced platforms that are driving the future of intelligent diagnostics. By synthesizing insights from the recent literature, the paper evaluates comparative performance metrics (e.g., sensitivity, resolution, adaptability); highlights integration strategies for embedded monitoring and multimodal sensing systems; and addresses challenges related to environmental sensitivity, data interpretation, and standardization. The transformative role of NDE 4.0 in enabling automated, real-time, and predictive structural assessment is also discussed. This review serves as a valuable reference for researchers and practitioners developing next-generation NDT/NDE solutions for hybrid and high-performance structures. Full article