Tool For Recognition Research Articles

Extensions and Detailed Analysis of Synergy Between Traditional Classification and Classification Based on Negative Features in Deep Convolutional Neural Networks

In recent times, deep convolutional neural networks became an irreplaceable tool for pattern recognition in many different machine learning applications, especially in image classification. On the other hand, these models are often used in critical systems which are the reason for new and recent research regarding their robustness and reliability. One of the most important issues for these models is their susceptibility to different adversarial attacks. In our previous work Milošević and Racković (Neural Network World. 2019;29(4):221–34), and Milošević and Racković (Neural Comput Applic. 2021;33:7593–602), the new type of learning applicable to all the convolutional neural networks was introduced: the classification based on the negative features and the synergy of traditional and those newly introduced network models. In the case of partial inputs/image occlusion, it was shown that our new method creates models that are more robust and perform better when compared to traditional models of the same architecture. In this paper, some extensions of the earlier proposed synergy are given by introducing negatively trained features and additional synergy between four independent neural network models. A detailed analysis of the robustness of the newly proposed model is performed on EMNIST and CIFAR-10 image classification data sets in the case of the selected input occlusions and adversarial attacks. The newly proposed neural network architecture improves the robustness of the neural network and increases its resistance to various types of input damage and adversarial attacks.

An Ensemble Kernelized-based Approach for Precise Emotion Recognition in Depressed People

As the COVID-19 pandemic created serious challenges for mental health worldwide, with a noticeable increase in depression cases, it has become important to quickly and accurately assess emotional states. Facial expression recognition technology is a key tool for this task. To address this need, this study proposes a new approach to emotion recognition using the Ensemble Kernelized Learning System (EKLS). Nonverbal cues, such as facial expressions, are crucial in showing emotional states. This study uses the Extended Cohn-Kanade (CK+) dataset, which was enhanced with images and videos from the COVID-19 era related to depression. Each of these images and videos is manually labeled with the corresponding emotions, creating a strong dataset for training and testing the proposed model. Facial feature detection techniques were used along with key facial measurements to aid in emotion recognition. EKLS is a flexible machine-learning framework that combines different techniques, including Support Vector Machines (SVMs), Self-Organizing Maps (SOMs), kernel methods, Random Forest (RF), and Gradient Boosting (GB). The ensemble model was thoroughly trained and fine-tuned to ensure high accuracy and consistency. EKLS is a powerful tool for real-time emotion recognition in both images and videos, achieving an impressive accuracy of 99.82%. This study offers a practical and effective approach to emotion recognition and makes a significant contribution to the field.

SeptAsTERS- SeptiCyte® RAPID as assessment tool for early recognition of sepsis - a prospective observational study.

Early recognition of sepsis is critical to patient outcome, with mortality increasing with every hour of delay in treatment. The aim of this study was to investigate the use of a point-of-care molecular host response assay to differentiate sepsis from inflammation after surgery. Three molecular host response assays (SeptiCyte® RAPID) were performed in 61 patients after major abdominal surgery with admission to the intensive care unit and drawn blood cultures. The first (T0) was taken ± 3h around the time of obtaining blood cultures, the second 24h later (T24) and the third at discharge from the intensive care unit (Tex). The primary endpoint was the agreement of SeptiCyte® RAPID results with the diagnosis of sepsis. SeptiScore® indicates sepsis probability (low risk 0 - high risk 15). Patients were retrospectively classified into sepsis and inflammation by three blinded experts. 25 (42.4%) patients were categorized as "inflammation" and 34 (57.6%) patients as "sepsis". At T0 and T24 septic patients showed significantly higher mean SeptiScores® of 8.0 (± 2.2 SD) vs. 6.3 (± 2.1 SD) and 8.5 (± 2.1 SD) vs. 6.2 (± 1.8 SD), respectively. The Receiver Operating Curves (ROC) for the ability to discriminate between sepsis and inflammation had an Area Under the Curve (AUC) of 0.71 (T0) and 0.80 (T24). Embedded in a comprehensive diagnostic algorithm molecular host response analysis could broaden the possibilities for infection diagnostics to differentiate between sepsis and inflammatory response after surgery. But validation in larger cohorts is needed.

Limits of speech in connected homes: Experimental comparison of self-reporting tools for human activity recognition

Data annotation for human activity recognition is a well-known challenge for researchers. In particular, annotation in daily life settings relies on self-reporting tools with unknown accuracy. Speech is a promising interface for activity labeling. In this work, we compare the accuracy of two commercially available tools for annotation: voice diaries and connected buttons. We retrofit the water meters of thirty homes in the USA for infrastructure-mediated sensing. Participants are split into equal groups and receive one of the self-reporting tools. The balanced accuracy metric is transferred from the field of machine learning to the evaluation of the annotation performance. Our results show that connected buttons perform significantly better than the voice diary, with 92% median accuracy and 65% median reporting rate. Using questionnaire answers, we highlight that annotation performance is impacted by habit formation and sentiments toward the annotation tool. The use case for data annotation is to disaggregate water meter data into human activities beyond the point of use. We show that it is feasible with a machine-learning model and the corrected annotations. Finally, we formulate recommendations for the design of studies and intelligent environments around the key ideas of proportionality and immediacy.

Bridging Classical Methodologies in Salmonella Investigation with Modern Technologies: A Comprehensive Review.

Advancements in genomics and machine learning have significantly enhanced the study of Salmonella epidemiology. Whole-genome sequencing has revolutionized bacterial genomics, allowing for detailed analysis of genetic variation and aiding in outbreak investigations and source tracking. Short-read sequencing technologies, such as those provided by Illumina, have been instrumental in generating draft genomes that facilitate serotyping and the detection of antimicrobial resistance. Long-read sequencing technologies, including those from Pacific Biosciences and Oxford Nanopore Technologies, offer the potential for more complete genome assemblies and better insights into genetic diversity. In addition to these sequencing approaches, machine learning techniques like decision trees and random forests provide powerful tools for pattern recognition and predictive modeling. Importantly, the study of bacteriophages, which interact with Salmonella, offers additional layers of understanding. Phages can impact Salmonella population dynamics and evolution, and their integration into Salmonella genomics research holds promise for novel insights into pathogen control and epidemiology. This review revisits the history of Salmonella and its pathogenesis and highlights the integration of these modern methodologies in advancing our understanding of Salmonella.

Machine-Learning-Assisted Materials Discovery from Electronic Band Structure.

Traditional methods of materials discovery, often relying on intuition and trial-and-error experimentation, are time-consuming and limited in their ability to explore the vast design space effectively. The emergence of machine learning (ML) as a powerful tool for pattern recognition has opened exciting opportunities to revolutionize materials discovery. This work explores the application of ML techniques to assist in the discovery of materials using band structure data. The electronic band structure, which describes the energy levels of electrons in a material, holds vital information regarding its electronic and optical properties. The band structure data of 63,588 materials, including metals and insulators, have been retrieved from the Materials Project database. The data were grouped into 85 batches based on the band path in the first Brillouin zone. Three ML clustering algorithms were trained on the band structure data after performing feature selection and engineering, followed by noise reduction. The models were validated by comparing the materials' properties in a cluster.

Validation of a diagnostic support tool for early recognition of cervical arterial dissection in primary care

BackgroundCervical arterial dissection (CeAD) is a leading cause of stroke in young adults with an early presentation often mimicking musculoskeletal pain. Currently, no validated tests exist and CAD may be missed. A diagnostic support tool could help guide urgent referral for imaging, when to monitor, or when safe to proceed with treatment, and ultimately help stroke prevention. ObjectivesTo validate a 4-criteria diagnostic support tool for early recognition of CeAD in primary care, to refine tool descriptors as needed, and propose optimal cut-offs for clinical application. DesignProspective observational study MethodParticipants with radiologically confirmed CeAD and controls without CeAD were identified from adults >18 years presenting to a tertiary metropolitan hospital with initial diagnosis of headache or neck pain. All were scored with criteria out of7 (acute onset pain [2], recent trauma/infection [1], neurological features [2], age <55 years [2]). Diagnostic values were calculated to determine cut offs and the tool was refined based on the analysis. ResultsThirty participants with CeAD and 261 controls with non-CeAD causes of headache and neck pain were included. The diagnostic support tool was an ‘excellent’ predictor of CeAD (AUC 0.83) but demonstrated poor specificity. Refining the tool to ‘acute/sudden onset’ [1], ‘unusual/unfamiliar headache/neck pain’ [1], recent trauma/infection [1] and neurological features ≥2 [1], scored out of 4, showed 100 % sensitivity and 74 % specificity to detect CeAD at a cut-off of 3/4 (AUC 0.87). ConclusionsThe refined tool shows acceptable clinical utility at a cut-off ≥3, where referral for vascular imaging is recommended. Further validation in Emergency and primary care is needed.

Fabric‐Reinforced Functional Insoles with Superior Durability and Antifracture Properties for Energy Harvesting and AI‐Empowered Motion Monitoring

AbstractFunctional triboelectric insoles hold promise for advancing self‐powered wearable technologies. However, their durability is compromised by continuous compressive forces and friction, leading to surface abrasion and material fracturing. To address these challenges, an innovative fabric‐reinforced structure combined with a dual‐L backrest design is developed that enhances anti‐fracture capabilities and electric outputs while enabling AI‐empowered motion monitoring. Polydimethylsiloxane (PDMS) is used as the negative triboelectric material with a dual‐L backrest design, while insulated copper wire (icuW) serves as the positive triboelectric material with an annular structure design. These components are intricately nested to enable a multilayered friction pairing. The fabric‐reinforced structure demonstrates excellent compressive rebound resilience, withstanding forces of at least 1000 N. The functional insole, featuring a fabric‐reinforced dual‐L backrest structure (FRdL‐insole), efficiently harvests biomechanical energy with a peak power of 8214 µW and maintains highly consistent performance after 10 washing cycles and 60 000 durability tests. It can power portable electronic devices such as digital watches, calculators, hygrometers, and LEDs. Enhanced with machine learning algorithms, the FRdL‐insole processes sensor signals to monitor human movements, accurately identifying seven distinct motions. This positions the insole as a smart, real‐time, self‐powered tool for activity recognition, showcasing its potential in intelligent wearable technology.

Denoising diffusion probabilistic model enhanced tool condition monitoring method under imbalanced conditions

Abstract In recent years, tool condition monitoring (TCM) based on deep learning has been widely considered and achieved remarkable success. However, these methods typically require relatively large training samples to produce significant results, which are both imbalanced and rather troublesome to obtain in the practical application of TCM. To address this issue, a novel TCM method combined with multiscale permutation entropy (MPE), denoising diffusion probabilistic model (DDPM) and a residual network (ResNet) is proposed under conditions of sample imbalance. First, the one-dimensional sensing signal data is converted to a grayscale recurrence plot (RP) by minimizing the MPE of the signals in each channel. Second, combine and splice these grayscale RPs from different channels in each sample into color RPs. After that, the generated RP images using DDPM are added to the imbalanced dataset to augment the data to achieve a balanced state of the dataset. Finally, the balanced mixed data set of real and fake samples is input into a ResNet for recognition and monitoring tool conditions. TCM experiments are conducted to verify the performance of the proposed method with imbalanced dataset, and the results of experimental investigation demonstrate that the accuracy of the proposed method improved by 2%–18.8% compared to that of the other four sample augmentation methods using ResNet18 when the imbalance rate is 1:200.

A New Deep Learning Methodology for Alarm Supervision in Marine Power Stations.

Marine engineering officers operate and maintain the ship's machinery during normal navigation. Most accidents on board are related to human factors which, at the same time, are associated with the workload of the crew members and the working environment. The number of alarms is so high that, most of the time, instead of helping to prevent accidents, it causes more stress for crew members, which can result in accidents. Convolutional Neural Networks (CNNs) are being employed in the recognition of images, which depends on the quality of the images, the image recognition algorithm, and the very complex configuration of the neural network. This research study aims to develop a user-friendly image recognition tool that may act as a visual sensor of alarms adjusted to the particular needs of the ship operator. To achieve this, a marine engineering simulator was employed to develop an image recognition tool that advises marine engineering officers when they are conducting their maintenance activities, with the aim to reduce their stress as a work risk prevention tool. Results showed adequate accuracy for three-layer Convolutional Neural Networks and balanced data, and the use of external cameras stands out for user-friendly applications.

Image Recognition Tools for Blind and Visually Impaired Users: An Emphasis on the Design Considerations

The existing digital image recognition technologies available for blind individuals are commercially accessible but still at an immature stage, necessitating enhancements in their capabilities. Areas requiring improvement in current image recognition tools for the visually impaired encompass information accuracy, information adequacy, appropriateness of information for blind individuals, functional sufficiency, and ergonomic suitability. This research endeavors to explore technology employing an inclusive approach to overcome limitations inherent in current digital image recognition technologies for the visually impaired. To streamline the research process, the initial phase involves a critical evaluation of deficiencies present in existing image recognition technologies for blind/visually impaired users through primary and secondary investigations. This strategic evaluation aims to identify key deficiencies, guiding assistive technology developers in focusing their efforts. Simultaneously, a survey is conducted to establish a comprehensive checklist of usability features and requirements for the proposed technology, aligning with the ISO 9241-110 standard. The overarching goal of this research is to address the question, "What design considerations should be taken into account in designing image recognition technology for blind and visually impaired people?" The research outcomes are intended to establish a standard for designers of digital products catering to blind/visually impaired users, fostering improved awareness and shaping attitudes toward individuals with visual disabilities in the development of image recognition software.

PredPVP: A Stacking Model for Predicting Phage Virion Proteins Based on Feature Selection Methods

Background: Phage therapy has a broad application prospect as a novel therapeutic method, and Phage Virion Proteins (PVP) can recognize the host and bind to surface receptors, which is of great significance for the development of antimicrobial drugs for the treatment of infectious diseases caused by bacteria. In recent years, several PVP predictors based on machine learning have been developed, which usually use a single feature to train the learner. In contrast, higher dimensional feature representations tend to contain more potential sequence information. Methods: In this work, we construct a stacking model PredPVP for PVP prediction by combining multiple features and using feature selection methods. Specifically, the sequence is first encoded using seven features. For this high-dimensional feature representation, three feature selection methods wereutilized to remove redundant features, then integrated with eight machine learning algorithms. Finally, probability features and class features (PCFs) generated by 24 base models were put into logistic regression (LR) to train the model. Results: The results of the independent test set indicate that PredPVP has higher performance compared to other existing predictors, with an AUC of 93.4%. Conclusion: We expect PredPVP to be used as a tool for large-scale PVP recognition, providing a new way for the development of novel antimicrobials and accelerating its application in actual treatment. The datasets and source codes used in this study are available at https://github.com/caoqian23/PredPVP.

Self-Replicating Catalytic Hybridization Assembly of Bipedal DNAzyme Walkers for Enhanced Electrochemiluminescence Bioanalysis.

Dynamic DNA nanodevices, particularly DNA walkers, have proven to be versatile tools for target recognition, signal conversion, and amplification in biosensing. However, their ability to detect low-abundance analytes in complex biological samples is often compromised by limited amplification depth and severe signal leakage. To address these challenges, we developed a simple yet highly efficient strategy to engineer a self-replicating bipedal DNAzyme (SEDY) walker for sensitive and selective electrochemiluminescence (ECL) bioanalysis. Unlike conventional DNA walkers that are typically constructed by catalytic DNA assembly in a single direction, the SEDY walker integrates a self-replicating feedback mechanism that greatly enhances both the selectivity and sensitivity of bioanalysis. First, the SEDY walker is assembled through a target-triggered, enzyme-free, self-replicating catalytic approach, minimizing the risk of undesired side reactions and signal leakage by simplifying reactant complexity. Furthermore, the SEDY walker features newly exposed trigger sequences that facilitate its autonomous replication, leading to a robust and exponential amplification of its products. Our experiments demonstrate that the SEDY walker can sensitively and selectively detect acetamiprid by navigating specific probes within cross-shaped DNA orbits. The ECL biosensor offers a linear detection range from 1 × 10-15 M to 1 × 10-9 M, with a limit of detection as low as 5.8 × 10-16 M. We anticipate that the SEDY walker will be a powerful tool for detecting various analytes in biological applications.

Data-driven fingerprint nanoelectromechanical mass spectrometry

Fingerprint analysis is a ubiquitous tool for pattern recognition with applications spanning from geolocation and DNA analysis to facial recognition and forensic identification. Central to its utility is the ability to provide accurate identification without an a priori mathematical model for the pattern. We report a data-driven fingerprint approach for nanoelectromechanical systems mass spectrometry that enables mass measurements of particles and molecules using complex, uncharacterized nanoelectromechanical devices of arbitrary specification. Nanoelectromechanical systems mass spectrometry is based on the frequency shifts of the nanoelectromechanical device vibrational modes that are induced by analyte adsorption. The sequence of frequency shifts constitutes a fingerprint of this adsorption, which is directly amenable to pattern matching. Two current requirements of nanoelectromechanical-based mass spectrometry are: (1) a priori knowledge or measurement of the device mode-shapes, and (2) a mode-shape-based model that connects the induced modal frequency shifts to mass adsorption. This may not be possible for advanced nanoelectromechanical devices with three-dimensional mode-shapes and nanometer-sized features. The advance reported here eliminates this impediment, thereby allowing device designs of arbitrary specification and size to be employed. This enables the use of advanced nanoelectromechanical devices with complex vibrational modes, which offer unprecedented prospects for attaining the ultimate detection limits of nanoelectromechanical mass spectrometry.

Intelligent Verification Tool for Surgical Information of Ophthalmic Patients-A Study Based on Artificial Intelligence Technology.

With the development of day surgery, the characteristics of "short, frequent and fast" ophthalmic surgery are becoming more prominent. However, nurses are not efficient in verifying patients' surgical information, and problems such as patient privacy leakage are becoming more prominent. To improve the situation, we developed a new augmented reality (AR)-based tool for visual recognition and artificial intelligent (AI) interpretation of the pattern and location of patient surgical skin markings for the verification of the correct surgical site and procedure. The tool can also display a variety of other verbally requested patient information. The purpose of this proposal is to evaluate its feasibility of use by surgical nurses in a real clinical setting. We developed a tool with image recognition technologies to interpretation patient surgical skin markings and match the information obtained with the patients surgical records, thus, verify the patient's surgical information. Verification includes the proper surgical site and type of procedure to be performed. Nurses can interact with the device through its speech recognition capabilities and the device provides them with a variety of other requested patient information via a heads-up display. Three hundred patients in an outpatient ophthalmology clinic were divided into an AR intelligent verification experimental group and a manual verification control group. The accuracy of information verification, work time consumption, and economic cost data were compared between the 2 groups to evaluate the effectiveness of the AR Surgical Information Intelligent Verification Tool in clinical patient surgical information verification. There was no statistically difference in the correct rates of patient surgical information review between the experimental group (95.33%) and the control group (98.67%) (χ2 = 2.934, P = 0.087). The median time for information verification was 10.00 (10.00, 11.00) seconds in the experimental group and 21.00 (20.00, 24.00) seconds in the control group, a statistically difference (Z = 0.000, P < 0.001). The experimental group saved 11 seconds per patient per review compared with the control group. Considering 10,531 surgeries in 2023, printing 1 page of surgical information per 9 patients and requiring 4 copies, 4680 pages of printing paper could be saved. The AR Surgical Information Intelligent Verification Tool has advantages in assisting medical staff in patient surgical information verification, improving nursing efficiency, preventing surgical mark errors or nonstandardization, protecting patient privacy, and saving costs. It has certain research and application value in the scenario of patient surgical information verification in ophthalmic day ward.

Artificial Internalizing Receptors: Intracellular Delivery of Cargo Through Bio-Orthogonal Recognition.

Drug targeting is a methodology that helps to overcome the side effects of therapeutic molecules. However, insufficient targeting specificity and the on-target/off-site delivery leave much room for improvement in the targeting endeavors. One approach to enhance the specificity of drug targeting is to engineer artificial receptors with recognition ligands not observed in nature. To this end, artificial internalizing receptors that feature cholesterylamine as the artificial pull-in mechanism, and an anti-fluorescein antibody as the exofacial recognition and capture tool are developed. Fluorescein labeling is among the most routine techniques in biochemistry and can readily provide a way to make cognate derivatives for receptor-mediated endocytosis using these artificial receptors. Herein, the synthesis and the structure-activity relationship for these artificial receptors are detailed, their potency and efficacy in mediating drug delivery for the antibody-drug conjugates are illustrated, and the scope and limitations of targeting the chemically engineered cells via artificial receptors are investigated. Taken together, the presented data explore an innovative approach to drug targeting and contribute to the development of techniques in cell engineering using the tools of chemistry.

Urban-Rural Disparities in Community Participation after Spinal Cord Injury in Ontario.

Personal, social, and environmental factors may influence self-efficacy and social reintegration among people living with spinal cord injury or disease (SCI/D) in urban and rural areas. Novel data collection methods have the potential to characterize community participation (CP) in diverse settings. The objectives were (1) to describe and compare self-reported community participation (Reintegration to Normal Living Index (RNLI) and Moorong Self-Efficacy Scale (MSES)) levels of individuals with SCI/D living in urban or rural Ontario, Canada; and (2) to determine the accuracy of an artificial intelligence (AI) optical mark recognition tool for extracting data from CP surveys conducted among participants after transitioning from inpatient rehabilitation to home and residing in the community. We partnered with SCI Ontario staff to collect MSES and RNLI survey data from adults with motor complete (e.g., AIS A-B) and incomplete (AIS C-D) SCI/D living in urban or rural Ontario, Canada, between January and October 2022. The Rurality Index of Ontario (RIO) from the postal code determined urban or rural residency. Optical mark recognition (OMR) software was used for survey data extraction. A Research Associate validated the extracted survey responses. Descriptive statistics, correlation analysis, and non-parametric statistics were used to describe the participants, their impairments, and their reported CP levels across urban and rural settings. Eighty-five individuals with SCI/D (mean age 53.7 years, 36.5% female) completed the survey. Most of the participants resided in major urban areas (69.4%) and had traumatic injuries (64.7%). The mean total MSES score for Ontarians with SCI/D was 87.96 (95% confidence interval [CI]: 84.45, 91.47), while the mean total RNLI score for the same individuals was 75.61 (95% CI: 71.85, 79.37). Among the MSES domains, the lowest score was observed in response to sexual satisfaction (mean: 4.012, 95% CI: 3.527, 4.497), while the lowest RNLI domain item score was associated with the ability to travel out of town (mean: 5.965, 95% CI: 5.252, 6.678). Individuals with incomplete injuries in rural areas reported lower MSES and RNLI scores than those with complete motor injuries, whereas no significant differences were found in MSES and RNLI scores among urban residents based on impairment. These findings suggest that, depending on the environmental context (e.g., rural vs. urban areas), AIS categories may influence the perception of CP among people living with SCI/D. The OMR tool had 97.4% accuracy in extracting data from the surveys. The CP (MSES and RNLI) scores reported by individuals with SCI/D differ based on their living setting. In rural Ontario, individuals with greater functional ability reported lower CP than their counterparts living in urban settings. Although CP remains a challenge, the needs of individuals with motor incomplete SCI/D and heterogeneous levels of mobility residing in rural areas require exploration and targeted interventions. The OMR tool facilitates accurate data extraction from surveys across settings.

TikTok and Bilibili as health information sources on gastroesophageal reflux disease: an assessment of content and its quality.

Gastroesophageal reflux disease (GERD) is a global chronic disease. Short video platforms make it easy for patients with GERD to obtain medical information. However, the quality of information from these videos remains uncertain. This study aimed to systematically assess videos related to GERD on TikTok and Bilibili. We conducted a search and gathered 241 Chinese videos related to GERD and recorded the essential information. Two independent evaluators assessed each video based on the completeness of six components of the GERD guidelines, and assessed the quality and reliability of the information in the videos using recognition tools. Finally, videos from different sources were compared. The uploaders of most videos were medical professionals (86.7%, n = 209). The content was mainly about symptoms and treatment. The quality of the videos information varied depending on the sources. Among videos posted on Bilibili, those posted by medical professionals had a lower content score for definition (P < 0.001). Videos produced by non-gastroenterologists had the highest mean modified DISCERN. (The DISCERN scoring tool was initially created for assessing written publications, but nowadays, it is frequently leveraged for appraising various health-related videos. Details can be found in the text) quality of the videos information was acceptable; however, the content varied significantly depending on the type of source used. Videos with broad content should be carefully screened to meet more needs.

Enhancing Stroke Recognition: A Comparative Analysis of Balance and Eyes-Face, Arms, Speech, Time (BE-FAST) and Face, Arms, Speech, Time (FAST) in Identifying Posterior Circulation Strokes.

Background/Objectives: Posterior circulation stroke (PCS) poses a diagnostic challenge due to the diverse and subtle clinical manifestations. While the FAST (Face, Arms, Speech, Time) mnemonic has proven effective in identifying anterior circulation stroke, its sensitivity to posterior events is less clear. Recently, the addition of Balance and Eyes to the mnemonic has been proposed as a more comprehensive tool for stroke recognition. Despite this, evidence directly comparing the effectiveness of BE-FAST and FAST in identifying PCS remains limited. Methods: A retrospective analysis was performed on stroke calls at a comprehensive stroke centre, Sydney, Australia. BE-FAST symptoms first assessed at an emergency department triage were recorded, along with automated acute computerised tomography perfusion (CTP) imaging findings. Haemorrhagic strokes were excluded from analysis. An ischaemic stroke diagnosis was confirmed 48-72 h later with magnetic resonance imaging (MRI) brain. The performance of 1. BE-FAST and FAST and 2. BE-FAST and CTP in the hyperacute detection of posterior circulation ischaemic stroke was compared. Results: Out of 164 identified ischaemic infarcts confirmed on MRIs, 46 were PCS. Of these, 27 were FAST-positive, while 45 were BE-FAST-positive. Overall, BE-FAST demonstrated a higher sensitivity compared to FAST in identifying PCS (97.8 vs. 58.7) but suffered from a lower specificity (10.0 vs. 39.8). Notably, 39.1% (n = 18) of patients with PCS would have been missed if only FAST were used. Furthermore, of the 26 PCS negative on CTP, 25 were BE-FAST-positive, and 14 were FAST-positive. Conclusions: The incorporation of Balance and Eye assessments into the FAST protocol improves PCS detection, although may yield more false positives.

AIEE-Active Green Fluorescence Imaging Technique for Latent Fingerprint Application.

Latent fingerprint imaging is a crucial tool for national security and crime recognition, requiring environmentally sustainable, nontoxic materials. To address this need, we have developed a green fluorescence-emitting material, THCHO, that binds effectively to latent fingerprints, enabling clear, high-resolution visualization. The solution produces strong green fluorescence that highlights fingerprint imprints with exceptional detail and contrast. The material is applicable across various substrates, making it efficient for on-site visualization. The synthesized organic conjugated material THCHO rapidly visualizes precise fingerprint substructures within 5 s using a simple spray method. It maintains high contrast and low interference across different substrates, such as glass, aluminum foil, plastic, and ironware, even in diverse backgrounds. Additionally, the THCHO material demonstrates high stability, retaining its fluorescence imaging capabilities for up to 35 days and after water washing. This is the first report of using a conjugated organic thiophene material for latent fingerprint fluorescence imaging, suggesting potential applications in other fields.