Search Results (1,841)

With the evolution of 6G technology toward global coverage and multidimensional integration, OTFS modulation has become a research focus due to its advantages in high-mobility scenarios. However, existing OTFS signal detection algorithms face challenges such as pilot contamination, Doppler spread degradation, and diverse interference in complex environments. This paper proposes the SPARSE-OTFS-Net algorithm, which establishes a comprehensive signal detection solution by innovatively integrating sparse random pilot design, compressive sensing-based frequency offset estimation with closed-loop cancellation, and joint denoising techniques combining an autoencoder, residual learning, and multi-scale feature fusion. The algorithm employs deep learning to dynamically generate non-uniform pilot distributions, reducing pilot contamination by 60%. Through orthogonal matching pursuit algorithms, it achieves super-resolution frequency offset estimation with tracking errors controlled within 20 Hz, effectively addressing Doppler spread degradation. The multi-stage denoising mechanism of deep neural networks suppresses various interferences while preserving time-frequency domain signal sparsity. Simulation results demonstrate: Under large frequency offset, multipath, and low SNR conditions, multi-kernel convolution technology achieves significant computational complexity reduction while exhibiting outstanding performance in tracking error and weak multipath detection. In 1000 km/h high-speed mobility scenarios, Doppler error estimation accuracy reaches ±25 Hz (approaching the Cramér-Rao bound), with BER performance of 5.0 × 10⁻⁶ (7× improvement over single-Gaussian CNN’s 3.5 × 10⁻⁵). In 1024-user interference scenarios with BER = 10⁻⁵ requirements, SNR demand decreases from 11.4 dB to 9.2 dB (2.2 dB reduction), while maintaining EVM at 6.5% under 1024-user concurrency (compared to 16.5% for conventional MMSE), effectively increasing concurrent user capacity in 6G ultra-massive connectivity scenarios. These results validate the superior performance of SPARSE-OTFS-Net in 6G ultra-massive connectivity applications and provide critical technical support for realizing integrated space–air–ground networks. Full article

With strategic mineral exploration extending to deep and complex geological settings, traditional methods increasingly struggle to dissect metallogenic systems and locate ore bodies precisely. This synthesis of current progress in muon imaging (a technology leveraging cosmic ray muons’ high penetration) aims to address these exploration challenges. Muon imaging operates by exploiting the energy attenuation of cosmic ray muons when penetrating earth media. It records muon transmission trajectories via high-precision detector arrays and constructs detailed subsurface density distribution images through advanced 3D inversion algorithms, enabling non-invasive detection of deep ore bodies. This review is organized into four thematic sections: (1) technical principles of muon imaging; (2) practical applications and advantages in ore exploration; (3) current challenges in deployment; (4) optimization strategies and future prospects. In practical applications, muon imaging has demonstrated unique advantages: it penetrates thick overburden and high-resistance rock masses to delineate blind ore bodies, with simultaneous gains in exploration efficiency and cost reduction. Optimized data acquisition and processing further allow it to capture dynamic changes in rock mass structure over hours to days, supporting proactive mine safety management. However, challenges remain, including complex muon event analysis, long data acquisition cycles, and limited distinguishability for low-density-contrast formations. It discusses solutions via multi-source geophysical data integration, optimized acquisition strategies, detector performance improvements, and intelligent data processing algorithms to enhance practicality and reliability. Future advancements in muon imaging are expected to drive breakthroughs in ultra-deep ore-forming system exploration, positioning it as a key force in innovating strategic mineral resource exploration technologies. Full article

Narrow-linewidth broadband tunable external cavity diode lasers (NBTECDLs), with their broadband tuning range, narrow linewidth, high side-mode suppression ratio (SMSR), and high output power, have become important laser sources in many fields such as optical communication, spectral analysis, wavelength division multiplexing systems, coherent detection, and ultra-high-speed optical interconnection. This paper briefly describes the basic theory of NBTECDLs, introduces NBTECDLs with diffraction grating type, fiber Bragg grating (FBG) type, and waveguide type, and conducts an in-depth analysis on the working principles and performance characteristics of NBTECDLs based on different NBTECDL types. Then, it reviews the latest research progress on Littrow-type, Littman-type, FBG-type, and waveguide-type NBTECDLs in detail and compares and summarizes the characteristics of Littrow-type NBTECDLs, Littman-type NBTECDLs, FBG-type NBTECDLs, and waveguide-type NBTECDLs. Finally, it looks at the structural features, key technologies, optical performance, and application fields of the most cutting-edge research in recent years and summarizes the challenges and future development directions of NBTECDLs. Full article

Ultra-wide bandgap β-Ga₂O₃ is a promising low-cost alternative to conventional direct X-ray detector materials that are limited by fabrication complexity, instability, or slow temporal response. Here, we comparatively investigate β-Ga₂O₃ thin films grown on c-sapphire by low-pressure chemical vapor deposition (LPCVD) and plasma-enhanced CVD (PECVD), establishing a quantitative linkage between growth kinetics, microstructure, defect landscape, and X-ray detection figures of merit. The LPCVD-grown film (thickness ≈ 0.289 μm) exhibits layered coalesced grains, a narrower rocking curve (FWHM = 1.840°), and deep-level oxygen-vacancy-assisted high photoconductive gain, yielding a high sensitivity of 1.02 × 10⁵ μC Gy_air⁻¹ cm⁻² at 20 V and a thickness-normalized sensitivity of 3.539 × 10⁵ μCGy_air⁻¹ cm⁻² μm⁻¹. In contrast, the PECVD-grown film (≈1.57 μm) shows dense columnar growth, higher O/Ga stoichiometric proximity, and shallow-trap dominance, enabling a lower dark current, superior dose detection limit (30.13 vs. 57.07 nGy_air s⁻¹), faster recovery, and monotonic SNR improvement with bias. XPS and dual exponential transient analysis corroborate a deep-trap persistent photoconductivity mechanism in LPCVD versus moderated shallow trapping in PECVD. The resulting high-gain vs. low-noise complementary paradigm clarifies defect–gain trade spaces and provides a route to engineer β-Ga₂O₃ thin-film X-ray detectors that simultaneously target high sensitivity, low dose limit, and temporal stability through trap and electric field management. Full article

This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative condition representing the most common cause of dementia and currently affects millions of people worldwide. The clinical presentation includes memory impairment, cognitive decline, and neuropsychiatric symptoms, reflecting pathological hallmarks such as β-amyloid (Aβ) plaques, neurofibrillary tangles, synaptic dysfunction, and neuroinflammation. Despite being the gold standard for detecting amyloid and tau pathologies in vivo, cerebrospinal fluid (CSF) biomarkers and positron emission tomography (PET) imaging are not widely used in the clinical setting because of invasiveness, high costs, and restricted accessibility. Recent advances in blood-based biomarkers offer a promising and minimally invasive tool for early detection, diagnosis, and monitoring of AD. Ultra-sensitive analytical platforms, including single-molecule arrays (Simoa) and immunoprecipitation-mass spectrometry, now enable reliable quantification of plasma Aβ isoforms, phosphorylated tau variants (p-Tau181, p-Tau217, p-Tau231), neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP). In addition, blood biomarkers reflecting oxidative stress, neuroinflammation, synaptic disruption and metabolic dysfunction are under active investigation. This narrative review synthesizes current evidence on blood-based biomarkers in AD, emphasizing their biological relevance, diagnostic accuracy, and clinical applications. Finally, we highlight forthcoming challenges, such as standardization, and future directions, including the use of artificial intelligence in precision medicine. Full article

Traditional X-ray fluorescence computed tomography (XFCT) suffers from issues such as low photon collection efficiency, slow data acquisition, severe noise interference, and poor imaging quality due to the limitations of mechanical collimation. This study proposes to design an X-ray fluorescence imaging system based on bilateral Compton cameras and to develop an optimized reconstruction algorithm to achieve high-quality 2D/3D imaging of low-concentration samples (0.2% gold nanoparticles). A system equipped with bilateral Compton cameras was designed, replacing mechanical collimation with “electronic collimation”. The traditional LM-MLEM algorithm was optimized through improvements in data preprocessing, system matrix construction, iterative processes, and post-processing, integrating methods such as Total Variation (TV) regularization (anisotropic TV included), filtering, wavelet-domain constraints, and isosurface rendering. Successful 2D and 3D reconstruction of 0.2% gold nanoparticles was achieved. Compared with traditional algorithms, improvements were observed in convergence, stability, speed, quality, and accuracy. The system exhibited high detection efficiency, angular resolution, and energy resolution. The Compton camera-based XFCT overcomes the limitations of traditional methods; the optimized algorithm enables low-noise imaging at ultra-low concentrations and has potential applications in early cancer diagnosis and material analysis. Full article

Apurinic/apyrimidinic endonuclease 1 (APE1) selectively cleaves the apurinic/apyrimidinic site (AP site) in DNA, playing a critical role in base excision repair and genomic stability maintenance. Aberrant APE1 expression has been linked to numerous diseases, including cardiovascular disorders, neurological conditions, and various cancers. However, existing methods for detecting trace levels of APE1 remain suboptimal for certain applications. To address this limitation, we developed an innovative biosensing platform for ultrasensitive APE1 detection by integrating APE1-specific site recognition with hybridization chain reaction (HCR)-based signal amplification, enabling enzyme- and label-free bioassays. In this system, APE1 recognizes and cleaves the AP site-containing hairpin (HP) probe, releasing a single-stranded HCR initiator that triggers cascaded HCR amplification. Owing to the high efficiency of HCR, this method achieves ultrahigh sensitivity, with a calculated detection limit of 1.0 × 10⁻⁸ U/mL. Furthermore, the biosensor demonstrates robust performance in cell lysates and is applicable for screening and evaluating APE1 inhibitors. Full article

Intravascular aspiration thrombectomy catheters are widely used to treat stroke, pulmonary embolism, and deep venous thrombosis. However, their performance is frequently compromised by clot material becoming lodged within the catheter tip. To address this, we develop a novel ultrasound-enhanced aspiration catheter approach that generates cavitation within the tip to mechanically degrade clots, with a view to facilitate extraction. The design employs hollow cylindrical transducers that produce inwardly propagating cylindrical waves to generate sufficiently high pressures to perform histotripsy. This study investigates the feasibility of self-sensing cavitation detection by analyzing voltage signals across the transducer during treatment. Experiments were conducted for two transmit pulse lengths at varying driving voltages with water or clot in the lumen. Cavitation clouds within the lumen were assessed using 40 MHz ultrasound imaging. Changes in the signal envelope during the pulse body and ringdown phases occurred above the cavitation threshold, the latter being associated with more rapid wave damping in the presence of bubble clouds within the lumen. In the frequency domain, voltage-dependent cavitation signals—subharmonics, ultra-harmonics, and broadband—emerged alongside transmit pulses. This work demonstrates a highly sensitive, sensor-free method for detecting cavitation within the lumen, enabling feedback control to further improve histotripsy-assisted aspiration. Full article

As a vital transition metal species, cobalt ions (Co²⁺) play a critical role in industrial and medical fields. However, uncontrolled release into ecosystems via industrial effluents presents significant environmental risks. To address this, a prism-coupled surface plasmon resonance (SPR) sensor chip was developed which enables simultaneous high sensitivity, wide detection range, and rapid detection of Co²⁺ under ultra-low detection limit conditions. By depositing a 50 nm Au film and AuNPs on a glass substrate, and integrating carboxyl-functionalized carbon quantum dots (CQDs), the chip achieved the detection range of 10⁻²⁰ mol/L to 10⁻⁴ mol/L, and the response time was reduced from 21 min to 11 min under optimal electric field conditions (1.2 V, 0.15 mol/L electrolyte concentration). The sensor exhibits high selectivity, repeatability, and stability. It can be integrated with optofluidic technology to enable high-throughput microfluidic analysis, thereby facilitating further advancements in related research. Full article

Underwater Object Detection (UOD) based on Synthetic Aperture Sonar (SAS) images is one of the core tasks of underwater intelligent perception systems. However, the existing UOD methods suffer from excessive model redundancy, high computational demands, and severe image quality degradation due to noise. To mitigate these issues, this paper proposes an ultra-lightweight and high-precision underwater object detection method for SAS images. Based on a single-stage detection framework, four efficient and representative lightweight modules are developed, focusing on three key stages: feature extraction, feature fusion, and feature enhancement. For feature extraction, the Dilated-Attention Aggregation Feature Module (DAAFM) is introduced, which leverages a multi-scale Dilated Attention mechanism for strengthening the model’s capability to perceive key information, thereby improving the expressiveness and spatial coverage of extracted features. For feature fusion, the Channel–Spatial Parallel Attention with Gated Enhancement (CSPA-Gate) module is proposed, which integrates channel–spatial parallel modeling and gated enhancement to achieve effective fusion of multi-level semantic features and dynamic response to salient regions. In terms of feature enhancement, the Spatial Gated Channel Attention Module (SGCAM) is introduced to strengthen the model’s ability to discriminate the importance of feature channels through spatial gating, thereby improving robustness to complex background interference. Furthermore, the Context-Aware Feature Enhancement Module (CAFEM) is designed to guide feature learning using contextual structural information, enhancing semantic consistency and feature stability from a global perspective. To alleviate the challenge of limited sample size of real sonar images, a diffusion generative model is employed to synthesize a set of pseudo-sonar images, which are then combined with the real sonar dataset to construct an augmented training set. A two-stage training strategy is proposed: the model is first trained on the real dataset and then fine-tuned on the synthetic dataset to enhance generalization and improve detection robustness. The SCTD dataset results confirm that the proposed technique achieves better precision than the baseline model with only 10% of its parameter size. Notably, on a hybrid dataset, the proposed method surpasses Faster R-CNN by 10.3% in mAP50 while using only 9% of its parameters. Full article

The sensitive detection of n-butanol is of high scientific and practical importance for ensuring safety in industrial production. In this study, hollow Cu₂O@CuS core–shell nanocubic heterostructures were fabricated via a multistep templating method. The Cu₂O@CuS heterostructures demonstrated exceptional performance, with an ultrahigh Brunauer–Emmett–Teller specific surface area that provided abundant active sites and a unique hollow architecture that enhanced mass transport and improved gas adsorption/desorption kinetics. High-density surface oxygen vacancies on the Cu₂O@CuS nanocubic heterostructures provide a key structural basis for the preferential adsorption of n-butanol molecules on its surface. The p–p heterojunction configuration further enhanced selective sensor response by optimizing the charge carrier separation and band structure modulation. The developed sensor achieved a detection limit of 3.18 ppm while exhibiting outstanding sensitivity, stability, and response time, meeting the stringent requirements for n-butanol detection in both industrial and agricultural settings. This work provides new insights on how to design materials for gas sensors. Full article

This article proposes a novel three-dimensional trench electrode detector, named the through-type three-dimensional quasi-hemispherical electrode detector. The detector adopts a trench structure to package each independent unit and achieves complete penetration of trench electrodes with the help of an SOI substrate. The horizontal distances from the center anode of the detector to the trench cathode and the detector thickness are equal. It has a near-spherical structure and exhibits spherical-like electrical performance. In this study, we modeled the device physics of the new structure and conducted a systematic three-dimensional simulation of its electrical characteristics, including the electric field, electric potential, electron concentration distribution of the detector, the inducted current caused by incident ions, and the crosstalk between detector units. Computational and technology computer-aided design (TCAD) simulation results show that the detector has an ultra-small capacitance (2.7 fF), low depletion voltage (1.4 V), and uniform electric field distribution. The trench electrodes electrically isolate the pixel units from each other so that the coherence effect between the units is small and can be applied in high-resolution X-ray photon counting detectors to enhance the contrast-to-noise ratio of low-dose imaging and the detection rate of tiny structures, among other things. Full article

This study analyzes the effects of washing conditions on polycyclic aromatic hydrocarbon (PAH) content in firefighter protective clothing. The analysis involved specially prepared textile packages made of materials used in such clothing: an outer shell, a moisture barrier membrane, and a thermal insulation lining. Package samples were subjected to simulated exposure to a selected group of PAH compounds. Ultra-high-performance liquid chromatography with fluorescence detection (UHPLC/FL) was applied to determine PAH content. The study showed that washing conditions (water temperature and the number of rinses) influenced the effectiveness of removal of chemical contaminants. The most favorable results were obtained for the washing process conducted at 60 °C with three rinse cycles, which resulted in the lowest concentration of total PAHs in the two examined types of textile packages (0.40 µg·g⁻¹ and 0.60 µg·g⁻¹ in the outer shell, 3.9 µg·g⁻¹ and 6.2 µg·g⁻¹ in the membrane, and 0.40 µg·g⁻¹ and 0.41 µg·g⁻¹ in the thermal lining of packages A and B, respectively). The higher washing temperature (60 °C) had a more favorable effect on average washing effectiveness as compared with the lower temperature (40 °C) in both the two- and three-rinse variants. The average washing effectiveness also varied according to the type of material and amounted to 70% and 54% for textile package types A and B, respectively. Full article

The performance and beam quality of the new fourth-generation synchrotron light source with ultra-low emittance are highly susceptible to coupled-bunch instabilities. These instabilities arise from the interaction between the bunched electron beam and the surrounding vacuum chamber installations. To mitigate these effects, the installation of a transverse bunch-by-bunch feedback system is planned. This system will comprise a button-type beam position monitor (BPM) for beam signal detection, a digital feedback controller, a broadband power amplifier, and a broadband stripline kicker as the primary actuator. One of the critical challenges lies in the development of the stripline kicker, which must be optimized for high shunt impedance and wide bandwidth while minimizing beam-coupling impedance. This work focuses on the comprehensive design of the stripline kicker intended for transverse (horizontal and vertical) bunch-by-bunch feedback in the Siam Photon Source II (SPS-II) storage ring. The stripline kicker design also incorporates features to enable its use for beam excitation in the SPS-II tune measurement system. The optimization process involves analytical approximations and detailed numerical electromagnetic field analysis of the stripline’s 3D geometry, focusing on impedance matching, field homogeneity, power transmission, and beam-coupling impedance. The details of engineering design are discussed to ensure that it meets the fabrication possibilities and stringent requirements of the SPS-II accelerator. Full article