Search Results (7,724)

Haemophilus influenzae (H. influenzae) is an important respiratory pathogen that can cause various invasive and non-invasive bacterial infections requiring rapid and sensitive detection. In recent years, electrochemical biosensors have emerged as a practical alternative for pathogen detection due to their high sensitivity, portability and short analysis time. Molecularly imprinted polymers (MIPs) are a class of synthetic receptors designed to mimic biological recognition through template-directed polymerization. In this study, an electrochemical biosensor based on MIPs was developed for the selective detection of H. influenzae. The polymeric film composed of methacrylamide (MAM), acrylamide (AAM), and vinylpyrrolidone (VP) monomers was fabricated on a gold screen-printed electrode (gold-SPE). The results of cyclic voltammetry (CV) revealed a strong redox current shift corresponding to bacteria concentrations within an analytical range of 1–10,000 CFU/mL with LOD 1.03 CFU/mL, with relative standard deviation (RSD) values below 9% across the tested concentration range. The optimized composition yielded and exhibited excellent selectivity when tested against non-target bacteria such as Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Full article

The monitoring of harmful algal blooms (HABs) constitutes a vital component of marine environmental protection and the sustainable development of the marine economy. However, the highly dynamic nature of these small targets, compounded by the complex water color interference prevalent in the coastal waters where HABs frequently occur, has resulted in traditional remote sensing monitoring methods, particularly those relying on fixed spectral index thresholds and pixel-wise binarization, suffering from imprecise identification in turbid coastal waters where suspended sediments, cloud cover, and sun glint create spectral confusion. These methods also exhibit low automation due to manual threshold adjustment requirements and poor transferability across different spatiotemporal conditions. Consequently, these methods struggle to meet practical application requirements. This study establishes a U-net model-based remote sensing identification framework for red tides using HY-1D CZI imagery (50 m resolution, 1–3 day revisit), targeted negative sample strategies, and event-level accuracy validation methods to achieve efficient marine red tide detection. Targeted negative sample selection involves purposefully selecting spectrally ambiguous regions as negative samples, aiming to enhance recognition accuracy and sample selection efficiency. The combination of targeted sampling with deep learning enables portability to new spatiotemporal contexts by learning invariant spectral–spatial features rather than relying on scene-specific thresholds. Experimental results demonstrate that the targeted negative sample strategy reduces event-level model false negatives by 27%, false positives by 36%, and increases the F1 score by 0.3217. Using an identical sample size, the targeted sample selection strategy yields an F1 score 0.0479 higher than random sampling. To achieve equivalent recognition accuracy, an increased number of random samples would be required. Comparative experiments reveal that the proposed method enhances sample selection efficiency by 87.5%. Transferability is demonstrated through successful identification of red tide patches in Wenzhou waters on 13 April 2022, without model retraining. This demonstrates that red tide remote sensing recognition based on targeted sample selection enables efficient, precise, and automated identification without human intervention, providing a reliable technical solution for operational marine red tide monitoring. Full article

Textile materials represent a versatile class of engineering substrates widely used in apparel, domestic products, and medical protective systems. Despite their extensive application, large-scale textile production has seen limited integration of fundamentally new functionalization strategies. In recent years, however, advances in materials science have enabled the development of textiles with tailored electrical, adaptive, and biological functionalities. This review summarizes recent progress in the functionalization of textile materials with a focus on approaches relevant to engineering and industrial implementation. Particular attention is given to conductive textiles designed for operation under extreme environmental conditions, including low-temperature climates. Methods for integrating electrically conductive elements into fibrous structures are discussed, highlighting their potential for sensing, thermal regulation, and energy-related applications such as powering portable electronic devices. Inkjet printing is presented as a scalable technique for high-resolution deposition of conductive patterns while preserving the mechanical integrity and aesthetic properties of textile substrates. In addition, adaptive and stimuli-responsive textile systems are reviewed, including materials capable of responding to thermal, optical, or chemical stimuli, with applications in camouflage, wearable systems, and multifunctional surfaces. The review further addresses the development of bioactive textiles, emphasizing antibacterial functionalization using organic and inorganic agents to mitigate the spread of pathogenic microorganisms. The relevance of such materials has been underscored by recent global viral outbreaks. Overall, this work aims to provide a materials science perspective on emerging textile functionalization strategies and to facilitate the transition of these technologies from laboratory-scale research to practical engineering applications. Full article

Bolted joints are extensively used in a wide range of industrial and commercial structures, making their condition monitoring essential for ensuring structural integrity and operational safety. Under the influence of vibration, cyclic loading, and environmental factors, bolts may gradually lose preload, which can degrade joint stiffness and eventually lead to structural failure. To address this issue, this study presents a smart percussion system developed on a Raspberry Pi platform that integrates acoustic signal acquisition, real-time signal processing, and visualization of diagnostic results. A bolt looseness detection strategy combining audio feature extraction with unsupervised learning is proposed. In contrast to traditional percussion-based approaches that depend on supervised learning and predefined baseline datasets, the proposed method does not require prior reference data, significantly improving its adaptability and ease of deployment across different structures, which shows essential practical significance. Experimental investigations demonstrate the effectiveness and advantages of the proposed system, indicating its strong potential to enhance percussion-based bolt looseness detection and to support real-time structural health monitoring, which are real-world engineering applications. Full article

Heavy-duty vehicles (HDVs) are major greenhouse gas emitters, and liquefied natural gas (LNG)-powered HDVs have emerged as a promising low-carbon alternative. However, their real-world emission performance and mitigation potential remain insufficiently studied, necessitating the characterization of LNG container trucks’ on-road CO₂ emissions via advanced sensing technologies. To characterize HDVs’ emission characteristics, real-driving emissions from China VI LNG and diesel-powered container trucks were measured employing portable emissions measurement systems (PEMS). The results reveal that high CO₂ emissions predominantly occur during low- to medium-speed acceleration and at speeds above 40 km/h with an acceleration exceeding 0.3 m/s² on highways, whereas emissions on port roads are more dispersed. A third-degree polynomial function fits emissions well with vehicle-specific power (VSP). Engine parameters mainly influence CO₂ emissions for LNG trucks, while VSP and acceleration significantly impact diesel trucks. The Random Forest model achieves superior prediction accuracy, particularly in highway scenarios, and significantly better CO₂ forecasting for LNG-powered trucks. These findings validate the effectiveness of PEMS-based sensing in characterizing low-carbon HDVs’ real-world emissions. The integration of multi-source sensor data and machine learning also provides a reference for intelligent sensing in transportation environmental monitoring. Full article

The TaiLing Mausoleum in Western Qing Tombs has great aesthetic value and a rich history. In this study, we conducted an analysis of the materials used in the architectural polychrome paintings of the TaiLing Mausoleum. Optical microscopy (OM), portable X-ray fluorescence (p-XRF), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDX), micro-Raman spectroscopy (μ-RS), and X-ray diffraction (XRD) were used to analyze the paintings of Long’en Gate in TaiLing Mausoleum. The results indicate that the main minerals in the ground layer are quartz, augite, feldspars and illite. The gilding materials employed gold leaf. The red pigment is hematite, and the black pigment is carbon black. The green pigment is emerald green with barium sulfate as an extender. The blue pigments are smalt and synthetic ultramarine. In some areas, emerald green is observed overlaying smalt, suggesting that the paintings at Long’en Gate underwent overlay restoration or repainting from the late Qing Dynasty to modern times. These results can support future conservation of the polychrome paintings at the TaiLing Mausoleum. Full article

Wind-induced noise remains a critical engineering challenge for MEMS microphones in compact consumer electronics such as smartphones, where spatial constraints limit conventional noise control solutions. This study experimentally investigates the suppression of flow-induced wind noise by a straight tube serving as the front cavity of a microphone, using a precision measurement microphone for data acquisition. Controlled experiments were conducted in both a flow duct for parametric isolation and an anechoic chamber for real-world validation. Results demonstrate a strong diameter-dependent effect: for a 1 mm diameter, increasing tube length significantly reduces noise power spectral density and steepens high-frequency roll-off via enhanced internal viscous and thermal dissipation. This effect weakens for a 2 mm diameter and becomes negligible for a 3 mm diameter, where noise is dominated by external flow excitation at the tube inlet rather than internal propagation. Therefore, extending tube length is an effective noise control strategy only for small-diameter cavities. Furthermore, while increased wind speed and oblique incidence elevate PSD, a longer tube reduces this sensitivity. Because acoustic transmission loss—including potential effects like aperture diffraction and impedance mismatch—was not measured, any resulting improvement in the effective signal-to-noise ratio is strictly presented as a hypothesis requiring future electroacoustic validation. The consistent findings across both experimental environments provide clear design guidance: for compact MEMS microphone systems in portable devices, elongating the front cavity is a viable passive noise control method only when the cavity diameter is sufficiently small (<2 mm). This offers a practical, space-efficient alternative to traditional windscreen-based approaches in portable devices. Full article

Environmental perception is a cornerstone for autonomous underwater vehicles (AUVs) to achieve robust self-localization and scene understanding, which are pivotal for the intelligent management of marine ranching. However, underwater image degradation and weak-textured scenes significantly hinder reliable self-localization and fine-grained environmental perception. To address the perceptual asymmetry arising from these challenges, this paper proposes a robust visual–inertial simultaneous localization and mapping (SLAM) and biomass assessment scheme for marine ranching. Specifically, we first propose a robust tightly coupled underwater visual–inertial localization scheme, which leverages a multi-sensor fusion strategy to solve the image degradation problem of localization in complex underwater environments. Furthermore, we propose a novel underwater scene perception method, which enables the simultaneous visual reconstruction of aquaculture species and the quantitative mapping of their spatial distribution in marine ranching. Finally, we develop a low-cost, agile, and portable multisensor-integrated system that consolidates autonomous localization and aquaculture biomass assessment modules, with its performance validated through extensive real-world underwater experiments. The experimental results demonstrate that the proposed methods can effectively overcome the interference of complex underwater environments and provide high-precision perception support for both AUV state estimation and aquaculture asset management. Full article

Open-source and commercial fifth-generation (5G) deployments are difficult to compare because they are built for different goals and reported under different conditions, which slows down validation and technology transfer from research to practice. This study explores the deployment and evaluation of two 5G Standalone (SA) disaggregated Radio Access Network (RAN) systems, using open-source research RAN, commercial RAN, and Software-Defined Radio (SDR) hardware. The first testbed is a SDR-based prototype, containing a Universal Software Radio Peripheral (USRP) B210 device, using Software Radio System RAN (srsRAN) as the RAN. The commercial-based testbed contains a Benetel RAN550 Radio Unit (RU), connected via an optical fiber to a Commercial Off-the-Shelf (COTS) server acting as the Distributed Unit (DU) and Centralized Unit (CU) using the Accelleran virtualized Baseband Unit (vBBU) platform. The Core Network (CN) is implemented using the open-source Open5GS in both testbeds. To evaluate the network’s functionality, throughput and latency are tracked using a Motorola Edge 50 Pro mobile terminal. The experimental results are analyzed and compared with representative performance metrics reported in the literature to place the measurements in a broader research context. This study further assesses trade-offs related to cost, portability, and scalability by comparing SDR-based research prototypes with commercial deployments. Full article

This work presents a compact microwave sensor for noninvasive blood glucose monitoring based on a substrate-integrated waveguide loaded with a complementary split-ring resonator on RO4350. The sensing principle uses shifts in resonance frequency and changes in S-parameters to track the dielectric dispersion of glucose-containing tissue. The resonator is constructed using Substrate-Integrated Waveguide (SIW) technology, which mimics the propagation characteristics of a conventional rectangular waveguide. To validate its versatility, the sensor implements three practical sample delivery modes: direct liquid contact with the sensing surface, a glass tube holder mounted over the active region, and a non-invasive fingertip interface. Electromagnetic simulations and benchtop measurements confirm clear glucose-dependent frequency shifts with stable matching and insertion levels. Across the physiological range of 20 to 200 mg·dL⁻¹, the sensor exhibits clear glucose-dependent resonance shifts in all configurations. In direct contact mode, the resonance frequency shifts from 10.83 GHz to 10.45 GHz with sensitivities up to 2.47 MHz per mg·dL⁻¹. The tube configuration shows a shift from 10.49 GHz to 10.38 GHz with sensitivity up to 0.80 MHz per mg·dL⁻¹, while reducing contamination. In the non-invasive fingertip mode, the resonance shifts from 2.56 GHz to 2.52 GHz with sensitivities up to 0.25 MHz per mg·dL⁻¹. These results confirm the sensor’s compactness, reliability, and suitability for portable, low-cost glucose monitoring. The results indicate that the proposed sensor can support practical continuous or spot monitoring and offers a clear path toward portable and low-cost glucose assessment. Full article

This study introduces a portable piezoelectric thin-film microbalance platform that combines acoustic signal analysis with deep learning for point-of-care mass detection. The system employs a flexible polyvinylidene fluoride sensor, a smartphone for acoustic signal acquisition, and three deep learning models: convolutional neural network, long short-term memory network, and Transformer. Experimental findings indicate that the Transformer achieves the highest classification accuracy of 99.5%, outperforming the convolutional neural network at 96.9% and the long short-term memory network at 97.3%, attributed to its enhanced capability to capture long-range temporal dependencies. The platform facilitates real-time, label-free detection without the necessity for bulky instrumentation, providing a cost-effective and scalable solution for decentralized diagnostics. This research establishes a foundational framework for intelligent portable micro-mass sensing with significant potential applications in precision medicine, environmental monitoring, and personalized healthcare. Full article

Portable toilets (PTs) play a crucial role in addressing global sanitation needs at events, construction sites, disaster areas, and remote locations. However, conventional cleaning products for PTs often contain harmful chemicals, which pose environmental risks. These substances negatively impact wastewater treatment plant (WWTP) microorganisms and overload treatment systems. Worldwide regulatory changes are pushing for products that are safe for both end-users and the environment. This trend is driving the need for new formulations and technologies in PT products. While popular PT cleaning solutions effectively control odors and pathogens, they often cause ecotoxicity and regulatory issues. Consequently, there is a growing need to explore alternative solutions free from the drawbacks of harmful chemicals. This review examines available environmentally friendly solutions and critically evaluates their potential for use in cleaning portable toilets. Biopreparations containing microorganisms and/or enzymes show exceptional promise. These solutions accelerate organic breakdown, increase the biodegradability of PTs wastewater, suppress odors, and reduce sludge volume. Transitioning to sustainable, bio-based cleaners is essential for environmental protection and regulatory compliance. Therefore, despite some limitations of biopreparations, it is envisioned that the future portable toilets will move towards engineered biopreparation for sustainable, chemical-free sanitation solutions. Full article

Portable Buddhist shrines refer to small-scale mobile or assembled shrines, typically made of wood, stone, clay, and metal. They were initially used as temporary ritual sites or ornamental attachments for temples and stupas, later becoming independent objects of devotion. This art form, the origins of which can be traced to ancient India and later diverse regional traditions, has been discovered in significant quantities along the Silk Road and neighboring regions. Previously, scholarly attention centered primarily on exquisite wall shrines, stupa-shaped shrines, and stele-shaped shrines. However, when factors such as the spatial arrangement and ritual functions of mobile ritual sites are taken into account, along with the materials and techniques employed in creating Buddhist shrines, artifacts such as badge-style bronze Buddha statues, painted silk banners, and wooden panel paintings may also be classified as portable Buddhist shrines. Accordingly, portable Buddhist shrines can be divided into three forms: pedestal, hanging, and open–close or mother–child. A key reason for this expanded classification is that all such forms are functionally and stylistically linked to large-scale cave temples. Moreover, these shrines share a common visual grammar, defined by the dynamic integration of images and texts and the mutual imitation and complementarity of statue and painting. This represents a quintessential example of cross-cultural dissemination and the coexistence of local traditions in Buddhist art. Full article

Point-of-care ultrasound (POCUS) is portable and radiation-free, but its clinical reliability is constrained by operator-dependent image acquisition and the limited scalability of expert quality assurance (QA) review. As handheld devices proliferate faster than mentorship capacity, trainees increasingly rely on heterogeneous free open access medical education (FOAMed) resources that rarely provide real-time psychomotor feedback. We conducted a structured narrative review (MEDLINE, Embase, Scopus, and Web of Science; last searched on 23 February 2026), with searches performed by H.H. and independently checked by J.J.P. (both POCUS-trained clinicians). After screening, 31 studies were included. We synthesized evidence on artificial intelligence (AI) systems that support bedside image acquisition and automate QA. The primary synthesis centered on key prospective or comparative clinical evaluations of AI-guided acquisition across echocardiography, focused assessment with sonography in trauma, abdominal aortic aneurysm screening, and lung ultrasound, complemented by peer-reviewed studies of FOAMed appraisal tools and online resource quality. These evaluations suggest that real-time probe guidance, view recognition, anatomy labeling, and automated capture may enable novices, after brief training, to acquire diagnostically adequate images for narrowly defined tasks. Early reports of automated QA scoring and program-level triage for expert review suggest potential to reduce expert workload and shorten feedback cycles, but external validation, generalizability across devices and patient habitus, and patient-centered outcomes remain limited. Acquisition-focused AI may therefore serve as an upstream “digital mentor” to improve novice image acquisition. We propose a practical pathway that integrates curated FOAMed resources and simulation with AI-guided bedside acquisition and continuous QA governance for safe deployment. Full article

To address the practical requirements for in situ equipment restoration, this study investigates a portable and cost-effective approach for the localized repair of SAE 10SAE 1045 components using a 1Cr13 martensitic stainless steel coating prepared via an arc-based additive manufacturing (WAAM) process. The microstructural evolution and tribological response of the layers were analyzed, with a focus on the effects of discrete thermal cycling and controlled solidification inherent to portable arc equipment. The WAAM process produced a refined martensitic matrix with a microhardness of 551.94 HV0.2, which is 2.26 times that of the substrate. Under dry sliding conditions, the 1Cr13 coating exhibited a lower friction coefficient and a reduced wear volume compared to the untreated SAE 1045, primarily through the mitigation of severe plastic deformation. This additive route provides a millimeter-scale reinforcement layer with metallurgical integrity suitable for heavy-duty service, aiming to offer a practical reference for the low-cost, on-site restoration of industrial components. Full article