Pregnancy monitoring is always essential for pregnant women and fetuses. According to the report of WHO (World Health Organization), there were an estimated 287,000 maternal deaths worldwide in 2020. Regular hospital check-ups, although well established, are a burden for pregnant women because of frequent travelling or hospitalization. Therefore, home-based, long-term, non-invasive health monitoring is one of the hot research areas. In recent years, with the development of wearable sensors and related data-processing technologies, pregnancy monitoring has become increasingly convenient. This article presents a review on recent research in wearable sensors, physiological data processing, and artificial intelligence (AI) for pregnancy monitoring. The wearable sensors mainly focus on physiological signals such as electrocardiogram (ECG), uterine contraction (UC), fetal movement (FM), and multimodal pregnancy-monitoring systems. The data processing involves data transmission, pre-processing, and application of threshold-based and AI-based algorithms. AI proves to be a powerful tool in early detection, smart diagnosis, and lifelong well-being in pregnancy monitoring. In this review, some improvements are proposed for future health monitoring of pregnant women. The rollout of smart wearables and the introduction of AI have shown remarkable potential in pregnancy monitoring despite some challenges in accuracy, data privacy, and user compliance.

Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for image-based remote sensing tasks. However, the requirement for large amounts of labeled data can limit the applicability of deep neural networks to existing remote sensing datasets. To overcome this challenge, few-shot learning has emerged as a valuable approach for enabling learning with limited data. While previous research has evaluated the effectiveness of few-shot learning methods on satellite-based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from Unmanned Aerial Vehicles (UAVs), which are increasingly used in remote sensing studies. In this review, we provide an up-to-date overview of both existing and newly proposed few-shot classification techniques, along with appropriate datasets that are used for both satellite-based and UAV-based data. We demonstrate few-shot learning can effectively handle the diverse perspectives in remote sensing data. As an example application, we evaluate state-of-the-art approaches on a UAV disaster scene dataset, yielding promising results. Furthermore, we highlight the significance of incorporating explainable AI (XAI) techniques into few-shot models. In remote sensing, where decisions based on model predictions can have significant consequences, such as in natural disaster response or environmental monitoring, the transparency provided by XAI is crucial. Techniques like attention maps and prototype analysis can help clarify the decision-making processes of these complex models, enhancing their reliability. We identify key challenges including developing flexible few-shot methods to handle diverse remote sensing data effectively. This review aims to equip researchers with an improved understanding of few-shot learning’s capabilities and limitations in remote sensing, while pointing out open issues to guide progress in efficient, reliable and interpretable data-efficient techniques.

We have designed, fabricated and characterized poly(dimethylsiloxane) (PDMS) arrayed waveguide grating (AWG) with four-channel output for operation in the visible light wavelength range. The PDMS AWG was realized based on the single-mode PDMS rib waveguide. The device was designed for 1 nm channel spacing with the wavelength ranging from 639 to 644 nm. The measured insertion loss is 11.4 dB at the peak transmission spectrum and the adjacent crosstalk is less than −16 dB. The AWG device occupies an area of 7.5 × 15 mm². PDMS AWG has the potential for integration with microfluidics in a monolithic PDMS lab-on-a-chip device for visible light spectroscopy applications.

Most of the microfluidics-related literature describes devices handling liquids, with only a small part dealing with gas-based applications, and a much smaller number of papers are devoted to the separation and/or detection of airborne inorganic particles. This review is dedicated to this rather less known field which has become increasingly important in the last years due to the growing attention devoted to pollution monitoring and air quality assessment. After a brief introduction summarizing the main particulate matter (PM) classes and the need for their study, the paper reviews miniaturized devices and/or systems for separation, detection and quantitative assessment of PM concentration in air with portable and easy-to-use platforms. The PM separation methods are described first, followed by the key detection methods, namely optical (scattering) and electrical. The most important miniaturized reported realizations are analyzed, with special attention given to microfluidic and micromachined or micro-electro-mechanical systems (MEMS) chip-based implementations due to their inherent capability of being integrated in lab-on-chip (LOC) type of smart microsystems with increased functionalities that can be portable and are easy to use. The operating principles and (when available) key performance parameters of such devices are presented and compared, also highlighting their advantages and disadvantages. Finally, the most relevant conclusions are discussed in the last section.

Trans-epithelial electrical resistance (TEER) is a good indicator of the barrier integrity of epithelial tissues and is often employed in biomedical research as an effective tool to assess ion transport and permeability of tight junctions. The Ussing chamber is the gold standard for measuring TEER of tissue specimens, but it has major drawbacks: it is a macroscopic method that requires a careful and labor intensive sample mounting protocol, allows a very limited viability for the mounted sample, has large parasitic components and low throughput as it cannot perform multiple simultaneous measurements, and this sophisticated and delicate apparatus has a relatively high cost. This paper demonstrates a low-cost home-made “sandwich ring” method which was used to measure the TEER of tissue specimens effectively. This method inspired the subsequent design of a biochip fabricated using standard soft lithography and laser engraving technologies, with which the TEER of pig epithelial tissues was measured. Moreover, it was possible to temporarily preserve the tissue specimens for days in the biochip and monitor the TEER continuously. Tissue responses after exposure tests to media of various pH values were also successfully recorded using the biochip. All these demonstrate that this biochip could be an effective, cheaper, and easier to use Ussing chamber substitute that may have relevant applications in clinical practice.

This paper reports a novel plasmonic sensor chip made up of a gold nanohole array chip coated with a WS₂monolayer, which is then functionalized for the detection of protein–protein interactions.