A visual-colorimetric-photothermal multimodal immunoassay for sensitive and quantitative detection of Escherichia coli.

This study examined how pedagogical strategies can support student-coaches’ (SCs) empowerment and promote preservice teachers’ (PSTs) professional learning within Sport Education (SE) seasons. Sixty-seven tenth-grade students (aged 15–18) participated in SE units taught by three PSTs (two males, one female, aged 22–25) enrolled in a master’s degree program in Teaching of Physical Education in Primary and Secondary Education in northern Portugal. Data were collected through participant observation, informal and focus group interviews, and PSTs’ reflective diaries within a Collaborative Action Research (CAR) framework and analyzed thematically. Three CAR cycles addressed key challenges: (1) encouraging SCs to assume responsibility for their role, (2) fostering inclusive and supportive team interactions, (3) strengthening SCs’ sport-specific and instructional knowledge. Guided by a facilitator, PSTs implemented strategies including pre-lesson meetings, structured communication routines, task-modification and feedback cards, accountability systems, and visual identification of SCs. Findings suggest that SCs’ empowerment was progressively constructed through interconnected psychological, relational and pedagogical processes, supported by structured mediation and iterative reflection. Simultaneously, engagement in CAR cycles enabled PSTs to develop adaptive instructional decision-making and mediation strategies. The study highlights how empowerment in SE is shaped through relational and pedagogical conditions and illustrates how CAR can foster reciprocal learning between SCs and PSTs in authentic teacher education contexts.

A cardiopulmonary exercise test (CPET) provides the estimated lactate threshold (θLT) and respiratory compensation point (RCP) through visual identification of multivariate gas exchange and ventilatory profiles. Artificial intelligence tools, such as deep neural networks, can learn, replicate, and classify these patterns and potentially aid in θLT and RCP identification, removing the subjectivity of threshold detection. This study evaluated a set of deep learning models (Oxynet) pre-trained with more than 1200 CPET files and tested its performance against visual inspection of experts. Evaluation included three phases: In phase I, 50 simulated ventilatory and gas exchange CPET files were generated, mixed with 50 authentic files, presented sequentially and in randomized order to three independent evaluators, and judged to be real or fake. In phase II, a new set of 50 files were generated, θLT and RCP were identified by both Oxynet and the consensus of three experts, and these estimates were compared with known values. In phase III, a subset of 163 CPETs were used to fine-tune Oxynet, and its evaluation of 50 independent authentic ramp CPET files were compared with those of the three experts. Experts correctly discriminated simulated from authentic data in 44% of cases (phase I). One-way ANOVA revealed no main effect of identified (known vs Oxynet vs human evaluators) for both θLT (p = 0.41) and RCP (p = 0.39) with ~ zero effect size for both θLT (ω2 = 0.00) and RCP (ω2 = 0.00) (phase II). Using real ramp-incremental data (phase III), the fine-tuned Oxynet identified the at 1944 ± 401 and 2555 ± 602mLmin-1 for θLT and RCP, respectively. Expert evaluators identified these at 1900 ± 469 and 2581 ± 625mLmin-1 with mean between-method biases of 45mLmin-1 (p = 0.087) and - 26 mLmin-1 (p = 0.118). Oxynet can be used as an accurate, reliable, and objective tool to identify or aid in the identification of exercise thresholds from gas exchange and ventilatory CPET data in healthy individuals.

Pirimicarb is a carbamate insecticide, widely used due to its specific control effect on aphid populations. However, the European Food Safety Authority conducted a risk assessment and proposed regulatory endpoints for it in October 2024. Therefore, there is an urgent need to develop rapid, sensitive, and convenient rapid detection technologies for pirimicarb. Thus, this study proposes an enzyme-free rapid detection method: using 4,5,6,7-tetrabromo-2',4',5',7'-tetraiodofluorescein (RB2) as a detection probe, since the synergistic effect of halogen and hydrogen bonding between RB2 and pirimicarb (PIB) in acidic aqueous solution induces charge transfer and leads to a distinct color change in RB2, thereby enabling the rapid detection of PIB. This method has good selectivity, and a limit of detection (LOD) of 0.0321 mg·L-1 in aqueous solution is achieved with a visual detection time of less than 60 s for PIB. And the LODs for PIB in cucumber and tomato peel samples are 0.0536 mg·L-1 and 0.0243 mg·L-1, respectively. Importantly, this method does not require enzymes as a vehicle in the detection process; it solely relies on the synergistic effect of halogen and hydrogen bonding between RB2 and PIB to achieve visual identification and detection of PIB, providing a reference method for the rapid detection of PIB.

Exposure to freshwater is a pressing health issue for coastal bottlenose dolphins ( Tursiops spp.). Environmental changes, including increased precipitation events and coastal infrastructure projects, are altering salinity within estuarine systems. Consequently, understanding effects of freshwater exposure on dolphins and developing tools to evaluate related health conditions is urgent. To address this need, a group of veterinarians, pathologists, epidemiologists, natural resource managers, and field biologists convened to create a protocol to visually assess freshwater-related skin lesions in free-ranging bottlenose dolphins. The Dolphin FRESH (Freshwater-Related Evaluation of Skin Health) Protocol guides users without medical backgrounds to screen and evaluate photographs by focusing on the visual identification of three primary indicators of freshwater skin disease: Overgrowth, Target-like Lesions, and Light Discoloration. By determining presence of the primary indicators and scoring associated characteristics, FRESH provides users with a relative assessment of the severity of these skin anomalies, and metrics to track progressive changes. The Scoring Rubric performed well during systematic testing, with evaluators correctly identifying freshwater cases through recognition of primary indicators and with no significant differences in total severity scores between field biologists and medical experts. FRESH is an important step in advancing knowledge on the effects of salinity fluctuations on dolphin health. When applied to photo datasets over time, this tool will enable researchers and managers to evaluate progression and regression of freshwater skin disease, occurrence and effects of multiple exposures, and the relationship between freshwater exposure skin indicators and health and survival outcomes.

Female genital schistosomiasis (FGS) is a sequela of infection with a waterborne parasite prevalent in sub-Saharan Africa and is associated with increased HIV risk. Diagnosis of FGS involves visual colposcopic identification of lesions on the cervix or vaginal walls. Previous studies have utilized digital image processing methods with statistical validation, and more recently, an artificial intelligence (AI)-based approach has also been explored. In this work, we sought to evaluate the performance of an AI model for identifying the presence of FGS from cervical photographs. Colposcopy images were obtained from 340 subjects in Zambia. Ground truth for presence or absence of FGS was determined by trained expert human examiners using visual assessment of images. Examiners also provided a FGS severity score between 0-8 for each image based on the number of lesions and the cervical quadrants affected, where 8 denotes highest severity and 0 denotes no FGS. The images were pre-processed with specular reflection artifact removal and image cropping to focus on the regions corresponding to the cervix and the transformation zone. The preprocessed dataset was randomly divided into training (FGS = 71, no FGS = 71) and testing (FGS = 21, no FGS = 177) cohorts. Image representations in the latent space were obtained using an ensemble of pre-trained machine learning models to further classify the image into FGS and no FGS. The best performance in the testing dataset was obtained at subject-level with area under the curve (AUC) =0.70 (95% Confidence interval: 0.58 - 0.82), Specificity = 0.68, and Sensitivity = 0.71, against the ground truth. Subjects with higher FGS severity scores (between 5-8) had high prediction rate by the machine classifier compared to those with lower severity scores (between 1-4). Machine learning shows promise in detecting FGS from limited colposcopy images. Early, accurate diagnosis may enhance reproductive health, and reduce HIV transmission risks, safeguarding maternal and child health.

With the continuous advancement of the “dual-carbon” strategy, the penetration of renewable energy sources such as wind and photovoltaic (PV) power has steadily increased, imposing more stringent requirements on the safe and stable operation of modern power systems. As the core components of these systems, critical electrical devices operate under harsh conditions characterized by high voltage, strong electromagnetic interference (EMI), and confined high-temperature environments. Their operating status directly affects the reliability of the power supply, and any fault may trigger cascading failures, resulting in significant economic losses. To address the issues of low inspection efficiency, limited fault-identification accuracy, and unstable data transmission in strong-EMI environments, this study proposes an intelligent inspection system for power equipment based on multi-technology integration. The system incorporates a redundant dual-mode wireless transmission architecture combining Wireless Fidelity (Wi-Fi) and Fourth Generation (4G) cellular communication, ensuring reliable data transfer through adaptive link switching and anti-interference optimization. A You Only Look Once version 8 (YOLOv8) object-detection algorithm integrated with Open Source Computer Vision (OpenCV) techniques enables precise visual fault identification. Furthermore, a multi-source data-fusion strategy enhances diagnostic accuracy, while a dedicated monitoring scheme is developed for the water-cooling subsystem to simultaneously assess cooling performance and fault conditions. Experimental validation demonstrates that the proposed system achieves a fault-diagnosis accuracy exceeding 95.5%, effectively meeting the requirements of intelligent inspection in modern power systems and providing robust technical support for the operation and maintenance of critical electrical equipment.

This study aims to develop a deep learning-based model for the automatic detection of fenestration and dehiscence in Cone Beam Computed Tomography (CBCT) images, providing a quantitative tool for diagnosing alveolar bone defects. Utilizing 10,752 manually annotated sagittal CBCT dental images, the Shifted Window Transformer U-Net (Swin UNETR) model was trained to automatically measure and diagnose fenestration and dehiscence. Model performance was evaluated based on key point localization accuracy, length measurement accuracy, and disease detection performance. Heatmaps were employed for visual identification of disease locations. The Swin UNETR model achieved key point recognition rates of 92.97%-99.09% for fenestration and dehiscence. Predicted lengths for all defect sites showed strong correlation with actual measurements. Disease diagnosis accuracy ranged from 0.8228 to 0.9476. The model demonstrated robust performance in key point identification, defect length quantification, and disease diagnosis. The deep learning model enables precise localization and quantitative measurement of fenestration and dehiscence in CBCT images. This approach enhances diagnostic efficiency and accuracy in detecting fenestration and dehiscence, facilitating preoperative orthodontic risk assessment and personalized treatment planning.

Polyethylenimine (PEI) is widely employed as a transfection reagent in recombinant adeno-associated virus (rAAV) manufacturing, but it must be removed from the final product due to its potential toxicity. Accurate quantification of PEI in complex biological matrices such as rAAVs is challenging, largely because the strong electrostatic attraction between PEI and nucleic acids can hinder the accuracy of its quantification. Here, we report a robust high-performance liquid chromatography method with charged aerosol detection for the quantification of residual linear PEI in purified AAV samples. Sample preparation includes treatment with trifluoroacetic acid and hydrochloric acid at 60°C to denature capsid protein, disrupt PEI-DNA polyplexes, and hydrolyze nucleic acids. The method achieves a limit of detection of 5 µg/mL and a limit of quantitation of 10 µg/mL in spike-and-recovery studies, with quantification confirmed via visual peak identification. This approach enables sensitive, specific, and reproducible PEI measurement and provides a valuable tool for process monitoring and quality control in gene therapy manufacturing.

Marek's disease, a highly contagious avian immunosuppressive disorder caused by the α-herpesvirus MDV-1, poses a significant threat to poultry health. The development of rapid visual detection methods capable of distinguishing epidemic MDV-1 strains from vaccine strains is crucial for early disease warning, vaccine efficacy evaluation, and precise disease control. We developed a novel isothermal detection system that integrates recombinase polymerase amplification (RPA) with CRISPR/Cas14a technology for the visual identification of epidemic MDV-1 strains. This method operates at a constant temperature of 37°C and allows for either real-time analysis or endpoint visual readout without the need for complex instrumentation. Our results showed no cross-reactivity with Newcastle disease virus, infectious bursal disease virus, MDV-1 vaccine strains, or herpesvirus of turkeys. Plasmid DNA standards were used to determine the sensitivity of the assay, and the detection limit was 24.6 copies/μL. Clinical evaluation using 24 field samples confirmed that the method successfully identified all Marek's disease virus-positive cases, demonstrating its diagnostic reliability. In conclusion, we have developed a rapid, highly specific nucleic acid detection platform for MDV-1 that enables visual readout without complex instrumentation by combining the sensitivity of RPA with the specificity of CRISPR/Cas14a technology, offering promising potential for field-based diagnostics and disease surveillance.IMPORTANCEMarek's disease virus (MDV-1) is a highly contagious and economically important avian pathogen. Existing diagnostic methods are unable to reliably distinguish between epidemic and vaccine strains in field settings, which hampers effective surveillance and evaluation of vaccination programs. To address this challenge, we developed a portable isothermal detection assay that combines recombinase polymerase amplification with CRISPR/Cas14a technology. This approach enables highly sensitive (24.6 copies/μL) and specific visual detection of epidemic MDV-1 strains without cross-reactivity with vaccine strains or related viruses. The assay demonstrated 100% agreement with reference methods when evaluated using clinical samples. As a cost-effective method that avoids the need for complex detection instruments, it offers a practical solution for rapid on-site diagnosis, facilitating enhanced outbreak control and improved poultry health management globally.

Chronic hepatitis B virus (HBV) infection is a major cause of liver-related morbidity and mortality worldwide. Serum HBV pregenomic RNA (pgRNA) is a surrogate marker for the transcriptional activity of covalently closed circular DNA. Here, we successfully designed a novel point-of-care (POC) diagnostic platform that allows specific, sensitive, rapid, and visual identification of HBV pgRNA. The platform integrates probe-based reverse transcription loop-mediated isothermal amplification (RT-LAMP) with either restriction endonuclease-mediated real-time fluorescence (REF) or a gold nanoparticle-based lateral flow biosensor (AuNPs-LFB), termed HBV-RT-LAMP. A unique set of probe-based LAMP primers targeting HBV-pgRNA was successfully designed. The optimal conditions for HBV-RT-LAMP were determined to be 64°C and 30 min. AuNPs-LFB and a pocket fluorescence detector (REF assay) were used for readout of the products. Our assay detected the target gene at concentrations as low as 50 copies/mL of HBV RNA standard and did not produce cross-reactions with HBV DNA (treated with DNase I) or other pathogens. The entire detection process, including HBV RNA extraction (45 min), LAMP (30 min), and the interpretation of results (AuNPs-LFB, less than 2 min), could be performed within 80 min, with no need for expensive devices. Therefore, the HBV-RT-LAMP diagnostic system developed in this study can potentially serve as a useful POC diagnostic tool for the evaluation of chronic HBV infection status and antiviral drug efficacy.IMPORTANCEChronic hepatitis B (CHB) is still a serious global concern that can result in severe liver-related diseases, including liver cirrhosis and hepatocellular carcinoma. Serum hepatitis B virus (HBV)-pregenomic RNA (pgRNA) has been proposed as a surrogate intrahepatic covalently closed circular DNA marker in CHB patients. Here, for the first time, a novel point-of-care diagnostic platform, termed HBV-reverse transcription loop-mediated isothermal amplification (RT-LAMP), which integrates probe-based RT-LAMP with either restriction endonuclease-mediated real-time fluorescence (REF) detection or a gold nanoparticle-based lateral flow biosensor (AuNPs-LFB), was developed and applied for accurate, sensitive, specific, rapid, and visual identification of HBV-pgRNA.

Abstract This paper presents the development and evaluation of a deep neural network model for the detection of naval surface vessel using laboratory-generated datasets. By employing chroma-key technology, images of a scale model naval vessel were superimposed onto realistic maritime backgrounds to create a diverse training dataset. Fine-tuned with these datasets and evaluated using the YOLOv8 framework, the model achieved high precision and recall in identifying the naval surface vessel despite data limitations. This zero-shot learning approach, validated through extensive testing, supports visual navigation and target identification in GPS/RF-denied environments, advancing autonomous maritime operations and aligning with the United States Navy strategy to leverage AI/ML for military enhancement.

ABSTRACT Identification of damage and key structural elements is vital to the monitoring and management of civil engineering projects, education, and training. However, practical inspection training is often constrained by cost, safety risk, and limited access to real structures, which reduces opportunities for repeated practice and feedback‐rich learning. To address these constraints, recent research has explored virtual reality (VR) in civil engineering to deliver immersive training for infrastructural inspections and reduce reliance on in‐person field trips and site visits. Despite the many advantages of VR as a learning tool, its adoption in civil engineering education remains limited. As a result, many engineers‐in‐training receive limited opportunities to practice realistic inspection workflows that combine defect recognition with structural health monitoring (SHM) interpretation. This paper presents a novel VR‐based educational tool designed to teach visual damage identification and structural condition assessment through immersive, scaffolded simulations. In this research, users explore a photorealistic 3D bridge reconstructed through drone‐based photogrammetry, annotate multiple damage types, and interact with embedded virtual sensors displaying multi‐year structural data collected from real‐world instrumentation. Unlike traditional approaches, the system integrates gamified scoring, real‐time feedback, and both qualitative and quantitative analysis tasks into a single, performance‐tracked learning experience. A classroom study with graduate students evaluated the tool's impact on learner motivation and confidence using a structured motivation model and a validated engineering self‐efficacy scale, demonstrating measurable improvements in damage assessment skills. This study advances the educational use of VR in civil engineering by combining interactive infrastructure scans, authentic sensor data, and experiential learning to offer a compelling, cost‐effective alternative to traditional field‐based inspection training.

Handicraft and Bihar, has a brilliant and unique connection, which adorns its aura. With the concept of brand in recent times, the handicraft sector too, needs to match with the need of the time, which is crucial for its longevity and sustenance. Thus, this paper is an attempt for Branding of Handicraft Brand, by exploring and developing factors responsible, for measuring its Brand Identity, in order to help in its Sustenance. This study highlights the importance and need of branding in the handicraft sector. Brand identity is crucial for the success of any product brand, hence, this study, advocates for Bihar handicraft (Sujani Embroidery of Bihar, with selected handicraft brand by Govt. of Bihar). The methodology adopted for this study is in-depth literature study from peer sources, for investigating the constructs responsible for development of brand identity followed under different product categories. Further, primary studies have been conducted in the form of Delphi Method, Questionnaire development, Survey and Factor Analysis, using Varimax rotation on SPSS. Based on the analysis, factors have been developed, which would help in defining the brand identity for handicraft brands, in order to help the brand in its identification by its stakeholders. This study is novel and unique, as no such study on handicraft sector has been done, which defines the factors responsible for measuring brand identity of handicraft brand, to the best of researcher’s knowledge.

Background: Intraoperative neuromonitoring (IONM) is an emerging alternative to visual identification of the recurrent laryngeal nerve (RLN) during surgery. Its goal is to reduce RLN injury and vocal cord paralysis (VCP). However, evidence has been inconsistent concerning its benefits. This study aims to assess IONM’s efficacy in preventing RLN injury during robotic thyroidectomy (RoT). Methods: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted across four electronic databases: PubMed, Web of Science, EMBASE, and Cochrane Library. The inclusion criteria comprised studies published in English that focused on RoT procedures utilizing IONM to assess their effect on RLN injury. Exclusion criteria included non-English publications, studies without full-text availability, reviews, editorials, case reports, animal studies, and studies that did not meet our objectives. After screening, data were extracted and presented qualitatively. Results: An analysis of six studies involving 1006 patients, where the majority evaluated IONM within RoT procedures. Continuous IONM (C-IONM) was feasible and effective for identifying RLN and aiding in its preservation. Outcomes such as success rates, electromyography signal quality, VCP incidence, hypoparathyroidism, hypocalcemia, and bleeding showed comparable trends across groups. Notably, a study found that RoT using IONM, particularly C-IONM, was associated with significantly longer operative times averaging approximately 55.8 minutes longer than open thyroidectomy. Despite this, findings emphasized the protective role of C-IONM in minimizing nerve injury during robotic thyroid procedures, although it did not significantly impact postoperative complications like VCP or length of stay. Conclusion: Our findings suggest that IONM, particularly C-IONM, is feasible during RoT and may support RLN identification and preservation. However, current evidence is limited, and further well-designed studies are needed to clarify its clinical impact and establish criteria for routine use.

Iatrogenic nerve injury in head and neck surgery remains a substantial yet potentially preventable source of morbidity across endocrine, otolaryngologic, and related procedures. This systematic review synthesized evidence to identify the nerves most frequently affected, delineate operative mechanisms of injury, describe the clinical course, and evaluate preventive strategies. Traction on visually intact nerves emerged as the predominant mechanism, with additional contributions from thermal injury, compression, ischemia, and entrapment by suture or clip. The recurrent laryngeal, facial, trigeminal (inferior alveolar and lingual), spinal accessory, and lower cranial nerves were identified as the principal structures at risk, with procedure type influencing the pattern and severity of deficits. Prevention centered on deliberate visual identification, meticulous dissection along natural planes, and risk-stratified use of intraoperative nerve monitoring, which proved most beneficial in complex or reparative fields. While most postoperative deficits resolved over time, a subset persisted, impairing voice, swallowing, facial movement, shoulder function, or orofacial sensation. Early, tension-free microsurgical repair was associated with superior recovery compared to delayed intervention. Despite heterogeneity in definitions, assessment timing, and follow-up, these findings support a practical prevention framework that integrates precise anatomical techniques, gentle handling, structured monitoring in high-risk cases, and timely referral for persistent deficits to minimize avoidable nerve injury and improve long-term outcomes.

Figure-ground reversal (FGR) transcends visual conventions by reversing the roles of figure and ground in brand logo designs. In this research, the authors study how FGR logos affect consumers’ brand attitudes. Using traditional self-reported measures as well as biometric technology, they illuminate the unique nature of FGR’s underlying mechanism and identify moderators to shed additional light on that process. Specifically, they find that the positive effect of FGR logos on brand attitude is mediated by engagement and aesthetic appeal, and moderated by the visual identification and semantic interpretability of FGR objects. Across a multi-method investigation that includes live bidding, incentive-compatible willingness-to-pay, eye-tracking, and multiple boundary condition experiments, the authors provide empirical support for these effects and reveal the underlying mechanism. They conclude by discussing the contributions of the research to the literature on visual marketing phenomena and the implications of the findings for better visual branding in the marketplace.

Face image classification is an important branch of computer vision and artificial intelligence, commonly applied in various fields such as facial recognition and facial attribute analysis. One facial attribute that is particularly interesting to classify is the use of eyeglasses, as it can affect the overall accuracy of facial recognition systems. This study aims to develop an eyeglass-use classification system based on a Convolutional Neural Network (CNN) and implement it in a web application using Flask to enable real-time prediction results. The research methodology includes collecting facial image datasets from Kaggle, performing preprocessing steps such as resizing, augmentation, and normalization, designing the CNN architecture, training the model, and evaluating its performance using a confusion matrix and classification report. The designed CNN model consists of three convolutional layers, max pooling, a flattening process, two fully connected layers, and a dropout layer to reduce the risk of overfitting. During the training phase, the model achieved 90% training accuracy and 96% validation accuracy, while testing on the test dataset resulted in an overall accuracy of 82%. The Flask-based system is capable of displaying real-time predictions, including the input image, classification label, accuracy percentage, and inference time. In the detection process, the accuracy of the model implementation reached 93% and the time required was in the range of a few milliseconds. The results demonstrate that the CNN can effectively classify faces with and without eyeglasses, and its implementation through a web interface offers broad potential for visual identification applications.

Methodology for the aerial photogrammetric counting of pacific salmon spawners with serial unmanned aerial vehicles (UAVs) is developed and tested for the rivers of southeastern Sakhalin on example of pink salmon Oncorhynchus gorbuscha. In total, 22 water streams with the total length of about 230 km were surveyed during the spawning migrations in July-August of 2022–2024. Traditional methods of the counting are labor-intensive and ineffective in this area because of hard relief and high afforestation of the river shores, but operational monitoring with consumer-class UAVs (DJI Phantom 4 Pro V2.0, DJI Mini 2, DJI Matrice 300 RTK) is available. The optimal parameters for aerial survey have been established: the flight altitude 20–100 m, UAV speed up to 6 m/s, longitudinal overlap of the images ≥ 80 % and the transverse overlap ≥ 40 %. The materials of 88 flight missions are processed in Agisoft Metashape Professional software package and orthophotoplans with resolution of 1.0–1.5 cm/pixel are obtained, suitable for visual identification and counting of fish using the geoinformation system NextGIS QGIS. Effectiveness of different UAV models is compared. The main limitations of the method concerned to weather conditions and the riverbed cover are defined. The developed methodology is an effective and economically feasible tool for operational control of spawning that can be used for the fishery regulation and evaluation of reproduction efficiency for pacific salmon in the Sakhalin Region.

Smartphone-integrated luminescent Europium-organic framework sensor for on-off ratiometric identification of levofloxacin in water and milk samples.