Public safety is a critical concern, typically addressed through security checks at entrances of public places, involving trained officers or X-ray scanning machines to detect prohibited items. However, many places like hospitals, schools, and event centres lack such resources, risking security breaches. Even with X-ray scanners or manual checks, gaps can be exploited by individuals with malicious intent, posing significant security risks. Additionally, traditional methods, relying on manual inspections and conventional image processing techniques, are often inefficient and prone to high error rates. To mitigate these risks, we propose a real-time detection model - EnhanceNet using a customized Scale-Enhanced Pooling Network (SEP-Net) integrated into the YOLOv4. The innovative SEP-Net enhances feature representation and localization accuracy, significantly contributing to the model\'s efficacy in detecting prohibited items. We annotated a custom dataset of nine classes and evaluated our models using different input sizes (608 and 416). The 608 input size achieved a mean Average Precision (mAP) of 74.10% with a detection speed of 22.3 Frames per Second (FPS). The 416 input size showed superior performance, achieving a mAP of 76.75% and a detection speed of 27.1 FPS. These demonstrate that our models are accurate and efficient, making them suitable for real-time applications.

Copper ions (Cu2+) pose significant risks to both human health and the environment as they tend to accumulate in soil and water. To address this issue, an innovative method using biomass-derived fluorescent carbon dots (D-CDs) synthesized via a hydrothermal process, with xylan serving as the carbon source was developed. D-CDs solution exhibited remarkable sensitivity and selectivity as a fluorescence sensor for Cu2+, boasting a low detection threshold of 0.64 μM. In order to facilitate real-time monitoring of Cu2+, solid-state fluorescent nanofiber membrane (NFD-CDs) through electrospinning was engineered. Additionally, D-CDs demonstrated successful Cu2+ detection in various real water samples, including those sourced from Xuanwu Lake, the Yangtze River, tap water, and bottled water, with accurate recovery rates observed. As a result, this research introduces a dual-mode analytical system for onsite detection of Cu2+ in real scenarios. By harnessing biomass-derived fluorescent CDs materials and solid-state fluorescence sensors, this approach offers a promising solution for addressing the challenges associated with Cu2+ contamination.

Mycoplasma pneumoniae is a significant pathogen responsible for community-acquired pneumonia, predominantly affecting children and adolescents. Here, we devised a rapid method for M. pneumoniae that combined multiple cross displacement amplification (MCDA) with real-time fluorescence technology. A set of ten primers, which were specifically designed for M. pneumoniae detection, were employed in a real-time fluorescence MCDA reaction. Of these, one primer incorporated a restriction endonuclease recognition sequence, a fluorophore, and a quencher, facilitating real-time fluorescence detection. The real-time (RT)-MCDA reactions were monitored in a simple real-time fluorescence instrument and conducted under optimised conditions (64°C for 40 min). The detection limit of the M. pneumoniae RT-MCDA assay for genomic DNA extracted from M. pneumoniae culture was down to 43 fg/µl. This assay accurately identified M. pneumoniae strains without cross-reacting with other bacteria. To validate its practical application, we tested the M. pneumoniae RT-MCDA assay using genomic DNA extracted from clinical samples. The assay\'s detection capability proved comparable with real-time PCR, MCDA-based biosensor detection, and visual inspection under blue light. The entire process, including rapid DNA extraction and real-time MCDA detection, was completed within 1 h. Overall, the M. pneumoniae RT-MCDA assay reported here is a simple and effective diagnostic tool for rapid M. pneumoniae detection, which holds significant potential for point-of-care testing and in resource-limited regions.

Micro-light-emitting diodes (μLEDs) have gained significant interest as an activation source for gas sensors owing to their advantages, including room temperature operation and low power consumption. However, despite these benefits, challenges still exist such as a limited range of detectable gases and slow response. In this study, we present a blue μLED-integrated light-activated gas sensor array based on SnO2 nanoparticles (NPs) that exhibit excellent sensitivity, tunable selectivity, and rapid detection with micro-watt level power consumption. The optimal power for μLED is observed at the highest gas response, supported by finite-difference time-domain simulation. Additionally, we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO2 NPs. The noble metals induce catalytic interaction with reducing gases, clearly distinguishing NH3, H2, and C2H5OH. Real-time gas monitoring based on a fully hardware-implemented light-activated sensing array was demonstrated, opening up new avenues for advancements in light-activated electronic nose technologies.

The inadequate stability of organic-inorganic hybrid perovskites remains a significant barrier to their widespread commercial application in optoelectronic devices. Aging phenomena profoundly affect the optoelectronic performance of perovskite-based devices. In addition to enhancing perovskite stability, the real-time detection of aging status, aimed at monitoring the aging progression, holds paramount importance for both fundamental research and the commercialization of organic-inorganic hybrid perovskites. In this study, the aging status of perovskite was real-time investigated by using terahertz time-domain spectroscopy. Our analysis consistently revealed a gradual decline in the intensity of the absorption peak at 0.968 THz with increasing perovskite aging. Furthermore, a systematic discussion was conducted on the variations in intensity and position of the terahertz absorption peaks as the perovskite aged. These findings facilitate the real-time assessment of perovskite aging, providing a promising method to expedite the commercialization of perovskite-based optoelectronic devices.

Papaya, renowned for its nutritional benefits, represents a highly profitable crop. However, it is susceptible to various diseases that can significantly impede fruit productivity and quality. Among these, leaf diseases pose a substantial threat, severely impacting the growth of papaya plants. Consequently, papaya farmers frequently encounter numerous challenges and financial setbacks. To facilitate the easy and efficient identification of papaya leaf diseases, a comprehensive dataset has been assembled. This dataset, comprising approximately 1400 images of diseased, infected, and healthy leaves, aims to enhance the understanding of how these ailments affect papaya plants. The images, meticulously collected from diverse regions and under varying weather conditions, offer detailed insights into the disease patterns specific to papaya leaves. Stringent measures have been taken to ensure the dataset\'s quality and enhance its utility. The images, captured from multiple angles and boasting high resolution are designed to aid in the development of a highly accurate model. Additionally, RGB mode has been employed to meticulously capture each detail, ensuring a flawless representation of the leaves. The dataset meticulously identifies and categorizes five primary types of leaf diseases: Leaf Curl (inclusive of its initial stage), Papaya Mosaic, Ring Spot, Mites (specifically, those affected by Red Spider Mites), and Mealybug. These diseases are recognized for their detrimental effects on both the leaves and the overall fruit production of the papaya plant. By leveraging this curated dataset, it is possible to train a model for the real-time detection of leaf diseases, significantly aiding in the timely identification of such conditions.

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R2 = 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management.

Hydrogen peroxide (H2O2) is a signaling molecule that has the capacity to control a variety of biological processes in organisms. Cancer cells release more H2O2 during abnormal tumor growth. There has been a considerable amount of interest in utilizing H2O2 as a biomarker for the diagnosis of cancer tissue. In this study, an electrochemical sensor for H2O2 was constructed based on 3D reduced graphene oxide (rGO), MXene (Ti3C2), and multi-walled carbon nanotubes (MWCNTs) composite. Three-dimensional (3D) rGO-Ti3C2-MWCNTs sensor showed good linearity for H2O2 in the ranges of 1-60 μM and 60 μM-9.77 mM at a working potential of -0.25 V, with sensitivities of 235.2 µA mM-1 cm-2 and 103.8 µA mM-1 cm-2, respectively, and a detection limit of 0.3 µM (S/N = 3). The sensor exhibited long-term stability, good repeatability, and outstanding immunity to interference. In addition, the modified electrode was employed to detect real-time H2O2 release from cancer cells and cancer tissue ex vivo.

Microbial biofilms, complex assemblies enveloped in extracellular matrices, are significant contributors to various infections. Traditional in vitro biofilm characterization methods, though informative, often disrupt the biofilm structure. The need to address biofilm-related infections urgently emphasizes the importance of continuous monitoring and timely interventions. This review provides a focused examination of advancements in real-time biofilm detection techniques, specifically in electrochemical, optical and mechanical systems. The potential applications of real-time detection in managing and monitoring biofilm growth in industrial settings, preventing medical infections, comprehending biofilm dynamics and evaluating control strategies highlight the necessity for it. Crucially, the review emphasizes the importance of evaluating these methods for their accuracy and reliability in real-time biofilm detection, offering valuable insights for precise interventions across various applications.
[Box: see text].

Sign language is an essential means of communication for individuals with hearing disabilities. However, there is a significant shortage of sign language interpreters in some languages, especially in Saudi Arabia. This shortage results in a large proportion of the hearing-impaired population being deprived of services, especially in public places. This paper aims to address this gap in accessibility by leveraging technology to develop systems capable of recognizing Arabic Sign Language (ArSL) using deep learning techniques. In this paper, we propose a hybrid model to capture the spatio-temporal aspects of sign language (i.e., letters and words). The hybrid model consists of a Convolutional Neural Network (CNN) classifier to extract spatial features from sign language data and a Long Short-Term Memory (LSTM) classifier to extract spatial and temporal characteristics to handle sequential data (i.e., hand movements). To demonstrate the feasibility of our proposed hybrid model, we created a dataset of 20 different words, resulting in 4000 images for ArSL: 10 static gesture words and 500 videos for 10 dynamic gesture words. Our proposed hybrid model demonstrates promising performance, with the CNN and LSTM classifiers achieving accuracy rates of 94.40% and 82.70%, respectively. These results indicate that our approach can significantly enhance communication accessibility for the hearing-impaired community in Saudi Arabia. Thus, this paper represents a major step toward promoting inclusivity and improving the quality of life for the hearing impaired.