Monitoring Of Progression Research Articles

Adaptive Top-K Algorithm for Medical Conversational Diagnostic Model.

With advancements in computing technology and the rapid progress of data science, machine learning has been widely applied in various fields, showing great potential, especially in digital healthcare. In recent years, conversational diagnostic systems have been used to predict diseases through symptom checking. Early systems predicted the likelihood of a single disease by minimizing the number of questions asked. However, doctors typically perform differential diagnoses in real medical practice, considering multiple possible diseases to address diagnostic uncertainty. This requires systems to ask more critical questions to improve diagnostic accuracy. Nevertheless, such systems in acute medical situations need to process information quickly and accurately, but the complexity of differential diagnosis increases the system's computational cost. To improve the efficiency and accuracy of telemedicine diagnostic systems, this study developed an optimized algorithm for the Top-K algorithm. This algorithm dynamically adjusts the number of the most likely diseases and symptoms by real-time monitoring of case progress, optimizing the diagnostic process, enhancing accuracy (99.81%), and increasing the exclusion rate of severe pathologies. Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system's response speed by 1.3-1.9 times compared to the state-of-the-art differential diagnosis systems.

A VIKOR-Based Decision Support System for Prioritizing Public Facility Improvements in Malang City with Geotagging Integration

Public facilities play a crucial role in driving economic growth and development. Nevertheless, the dearth of public information concerning facility enhancements fosters a sense of public distrust towards the government. Additionally, numerous facilities, which should be prioritized for improvement, have not received adequate attention. In contrast to several prior studies, the present study encompasses a broader scope and incorporates geotagging techniques to precisely identify the location of complaints and determine the optimal route to reach them. Moreover, an analysis process utilizing the VIKOR method has been devised to assess the priority of public facility improvements. This method yielded an accuracy rate of 89,7%, signifying a commendable level of precision and a 16% increase in accuracy based on confusion matrix method compared to previous studies. Through user usability testing, it was determined that the majority of users agreed that this system can facilitate public reporting, enable progress monitoring of public facility improvements, and aid in prioritizing such improvements.

Evaluating English Teachers’ Artificial Intelligence Readiness and Training Needs with a TPACK-Based Model

With the rapid development and widespread adoption of artificial intelligence (AI) tools, the implementation of instructional pedagogy has transformed significantly. English teachers need to understand how AI tools can improve their teaching and must acquire the necessary technical and pedagogical knowledge to effectively utilize AI technology. Although the integration of AI into language teaching shows potential benefits, there remains a dearth of comprehensive research on English teachers’ perceptions, readiness, and professional development requirements in relation to AI.To address these knowledge research gaps, our study aims to evaluate English teachers’ current understanding of AI tools and their training needs for integrating AI into the English language classroom. Our proposed model uses the technological pedagogical content knowledge (TPACK) framework, which incorporates English language teaching and information literacy contexts. This framework allows for a holistic assessment of teachers’ readiness for integrating AI within English language teaching practices.A study was conducted with a class of preservice English teachers in Hong Kong. An online survey was designed to assess the readiness of English teachers for applying AI tools in the classroom as well as their understanding and level of information literacy. This study helped identify and address potential issues with the survey before launching it to a wider audience. Our findings confirmed the validity and reliability of the instrument and indicated that preservice English teacher participants are generally prepared to integrate AI tools into the English classroom. Corelation analysis was also conducted to assess the relationships among the constructs and showed that technological pedagogical knowledge (TPK) and instructional literacy (IL) were significant predictors of the overall TPACK construct. The study suggested professional training in the selection, implementation and progress monitoring of specific AI tools for English Language teaching; pedagogy design; and the ability to search for appropriate resources for the English classroom. The framework can be enhanced by using a mixed-method approach and incorporating a qualitative study to triangulate the findings. An explanatory sequential design will be recommended to collect quantitative data first, then qualitative data will be collected for further analysis.

Automated monitoring of tooth wear progression using AI on intraoral scans

ObjectivesThis study aimed to develop and evaluate a fully automated method for visualizing and measuring tooth wear progression using pairs of intraoral scans (IOSs) in comparison with a manual protocol. MethodsEight patients with severe tooth wear progression were retrospectively included, with IOSs taken at baseline and 1-year, 3-year, and 5-year follow-ups. For alignment, the automated method segmented the arch into separate teeth in the IOSs. Tooth pair registration selected tooth surfaces that were likely unaffected by tooth wear and performed point set registration on the selected surfaces. Maximum tooth profile losses from baseline to each follow-up were determined based on signed distances using the manual 3D Wear Analysis (3DWA) protocol and the automated method. The automated method was evaluated against the 3DWA protocol by comparing tooth segmentations with the Dice-Sørensen coefficient (DSC) and intersection over union (IoU). The tooth profile loss measurements were compared with regression and Bland-Altman plots. Additionally, the relationship between the time interval and the measurement differences between the two methods was shown. ResultsThe automated method completed within two minutes. It was very effective for tooth instance segmentation (826 teeth, DSC = 0.947, IoU = 0.907), and a correlation of 0.932 was observed for agreement on tooth profile loss measurements (516 tooth pairs, mean difference = 0.021mm, 95% confidence interval = [-0.085, 0.138]). The variability in measurement differences increased for larger time intervals. ConclusionsThe proposed automated method for monitoring tooth wear progression was faster and not clinically significantly different in accuracy compared to a manual protocol for full-arch IOSs. Clinical significanceGeneral practitioners and patients can benefit from the visualization of tooth wear, allowing quantifiable and standardized decisions concerning therapy requirements of worn teeth. The proposed method for tooth wear monitoring decreased the time required to less than two minutes compared with the manual approach, which took at least two hours.

Measuring Elementary Student Reading Gain in the Context of School-Implemented Multitiered Systems of Support

There is limited research with mixed results about student reading gains in Grades 1 to 5 within typical school-implemented Response to Intervention (RTI). As part of a larger study, we used school-administered screening data on a widely used computer-adaptive test (Measures of Academic Progress) to describe reading gains across one academic year. We observed relatively faster growth in the first grade than in the later grades. We found stronger growth for students not identified with a disability relative to students with low or high-incidence disabilities or a 504 plan. We explored moderation of gains for students receiving tiered interventions based on administrators’ interviews about RTI implementation. We observed that better ratings of administrators’ articulation about using the evidence-aligned RTI components of progress monitoring, data-based decision-making, and Tier 2 interventions were related to greater gains for students receiving Tier 2 or 3 intervention. We discuss implications, limitations, and directions for future research.

Tracking the pre-clinical progression of transthyretin amyloid cardiomyopathy using artificial intelligence-enabled electrocardiography and echocardiography.

Diagnosing transthyretin amyloid cardiomyopathy (ATTR-CM) requires advanced imaging, precluding large-scale testing for pre-clinical disease. We examined the application of artificial intelligence (AI) to echocardiography (TTE) and electrocardiography (ECG) as a scalable strategy to quantify pre-clinical trends in ATTR-CM. Across age/sex-matched case-control datasets in the Yale-New Haven Health System (YNHHS) we trained deep learning models to identify ATTR-CM-specific signatures on TTE videos and ECG images (area under the curve of 0.93 and 0.91, respectively). We deployed these across all studies of individuals referred for cardiac nuclear amyloid imaging in an independent population at YNHHS and an external population from the Houston Methodist Hospitals (HMH) to define longitudinal trends in AI-defined probabilities for ATTR-CM using age/sex-adjusted linear mixed models, and describe discrimination metrics during the early pre-clinical stage. Among 984 participants referred for cardiac nuclear amyloid imaging at YNHHS (median age 74 years, 44.3% female) and 806 at HMH (69 years, 34.5% female), 112 (11.4%) and 174 (21.6%) tested positive for ATTR-CM, respectively. Across both cohorts and modalities, AI-defined ATTR-CM probabilities derived from 7,423 TTEs and 32,205 ECGs showed significantly faster progression rates in the years before clinical diagnosis in cases versus controls (p time × group interaction ≤0.004). In the one-to-three-year window before cardiac nuclear amyloid imaging sensitivity/specificity metrics were estimated at 86.2%/44.2% [YNHHS] vs 65.7%/65.5% [HMH] for AI-Echo, and 89.8%/40.6% [YNHHS] vs 88.5%/35.1% [HMH] for AI-ECG. We demonstrate that AI tools for echocardiographic videos and ECG images can enable scalable identification of pre-clinical ATTR-CM, flagging individuals who may benefit from risk-modifying therapies.

Plasma proteomic profiles of patients with HIV infection and coinfection with hepatitis B/C virus undergoing anti‑retroviral therapy.

Chronic liver disease is becoming a leading cause of illness and mortality in patients living with human immunodeficiency virus (HIV; PLWH) undergoing suppressive anti-retroviral therapy. Its primary etiology is coinfection with hepatitis B and C virus (HBV and HCV, respectively). Chronic liver inflammation and fibrosis can potentially lead to the development of hepatocellular carcinoma (HCC). Therefore, monitoring of the disease progression in PLWH is required. The present study aimed to explore plasma protein profiles of PLWH and those coinfected with HBV and HCV using shotgun proteomics. HIV-monoinfected, HIV/HBV-coinfected, HIV/HCV-coinfected and uninfected control individuals were recruited. Patients in the three virus-infected groups had significantly higher levels of liver fibrosis indices (fibrosis-4 score and aspartate aminotransferase to platelet ratio index) compared with the control group. Liquid chromatography-tandem mass spectrometry analysis of plasma samples identified 1,074 proteins that were differentially expressed, where subsequent partial least squares-discriminant analysis model demonstrated clear clustering of proteomes from the four sample groups; 18 proteins that were significantly differentially expressed. Heatmap analysis identified two main groups of proteins, six proteins being upregulated only in the HIV/HBV-coinfection group and 10 proteins downregulated in all three virally infected groups. STITCH 5.0 analysis predicted an interaction network containing two identified proteins in the latter group, specifically ubiquitin interaction motif-containing 1 (UIMC1) and haptoglobin (HP), which are part of the profibrogenic TGF-1β/SMAD, inflammatory TNF and tumor suppressor BRCA1 pathways. Expression levels of UIMC1 and HP were significantly lower in HIV-infected groups compared with those in uninfected controls. Altogether, these proteomics data provide protein expression profiles potentially associated with HIV infection and coinfection with HBV/HCV, which may be applied to predict progression to advanced liver disease or HCC in PLWH.

Where Are We Now with the Implementation of the Talent Development Framework for Gifted Students and Where Do We Go in the Future?

Talent development as a framework for services for advanced learners has gained traction within schools, but there are challenges that remain. In this paper, we address some of these, including identification systems that are consistent with a domain focus and geared towards the stage of talent development; exceptional abilities that are often ignored by schools but could be identified and cultivated; programming that is continuous, articulated with the school curriculum, with defined outcomes for growth and performance; better understanding and use of data for identification and monitoring of progress; and policies that support acceleration and advanced learning options. The major challenge for schools is the potential to bifurcate services into talent development for underserved learners versus gifted services for high achieving students, rather than providing multiple pathways for learners with different needs that lead to high achievement for all.

Rehab-AMD: co-design of an application for visual rehabilitation and monitoring of Age-related Macular Degeneration

BackgroundThe increasing demand for remote medical care, driven by digital healthcare advancements and the COVID-19 pandemic, necessitates effective solutions tailored to patients and healthcare practitioners. Co-design, involving collaboration between software developers, patients, and healthcare practitioners, prioritizes end-user needs. Research indicates that integrating patient perspectives enhances user experience and usability. However, its application in healthcare has been limited to small projects. This work focuses on co-designing a technological solution to enhance the monitoring and visual rehabilitation of individuals with Age-Related Macular Degeneration (AMD), a condition that significantly impacts the quality of life in people over 60. Current vision rehabilitation systems lack personalization, motivation, and effective progress monitoring. Involving patients and healthcare practitioners in the design process aims to ensure the final product meets their needs.MethodsThe project employs iterative and collaborative principles, involving a vision rehabilitation expert and two AMD patients as active users in the application’s development and validation. The process begins by establishing requirements for user accounts and rehabilitation exercises. It continues with an initial approach extended through user validation. Co-design is facilitated by specific workshops marking each project iteration, totaling four workshops, along with continuous communication sessions between experts and developers to validate design decisions. Initial requirements gathering and constant feedback from end-users, the visual rehabilitator, and patients are crucial for refining the product effectively.ResultsThe workshops produced a prototype featuring a test to monitor changes and progression and 15 visual rehabilitation exercises. Numerous patient and vision rehabilitation-driven software modifications led to a final design that is responsive and adaptive to end-user needs.ConclusionsThe Rehab-AMD pilot project aims to develop a collaborative and adaptive software solution for AMD rehabilitation by actively involving stakeholders and applying iterative design principles. Co-design in the Rehab-AMD solution proves to be a methodology that identifies usability issues and needs from the initial design stages. This approach ensures that software developers create a final product that is genuinely useful and manageable for people with AMD and the targeted vision rehabilitators.

Deep learning-based computer vision in project management: Automating indoor construction progress monitoring

Progress monitoring is crucial for effective project management, particularly in construction projects. The adoption of computer vision with deep learning expedites automation, accuracy, and efficiency in construction progress monitoring by overcoming the challenges of laborious, and error prone manual methods. While there is growing attention on developing computer vision based deep learning models for construction progress monitoring, deployment platforms for project managers are lacking. Using computer vision, this study develops a Mask Recurrent Convolutional Neural Network deep learning model. It utilizes progress images of drywall construction from two indoor construction sites and tests the model on a third indoor site in Sydney, Australia. The model is capable of automated as-built visual detection and work-in-progress measurement. The study also provides an understanding on the deployment process of the deep learning model on a cloud-based platform called Streamlit. By developing a model tailored for automatically quantifying work-in-progress of indoor construction elements and detailing the process of deploying that model on a cloud-based platform, this study significantly advances digitalization of construction project management. Project managers, stand to benefit from these advancements by gaining access to more accurate and automated construction progress monitoring for better decision-making.

Initial Validation of the New Jersey School Climate Improvement Survey

School climate has a profound impact on the wellbeing of staff and students. Although existing measures are available to assess dimensions of school life (Bradshaw et al., 2021; Espelage et al., 2014), surveys that comprehensively assess components of school climate from multiple perspectives are needed. A rationale is presented for developing the New Jersey School Climate Improvement Survey for students, staff, and parents/caregivers, which varies from existing instruments in its measurement of unique domains; alignment of items across informant types; integration of diver­sity, equity, and inclusion items across domains; and ability to disaggregate by demographic characteristics, including student gender expansive categories. Internal consistency and structural validity were investigated using data from 5,504 students in grades 3-5, 16,280 students in grades 6-12, 5,464 staff, and 5,874 parents/caregivers. For each survey version, results support a correlated model structure and demonstrate strong internal reliability (e.g., omega coefficients above 0.70). Confirmatory factor analyses and Pearson correlations demonstrate strong internal structural validity across surveys. Implications are discussed, including how the instrument can inform a school climate improvement process that involves identifying strengths and needs, assessing the impact of interventions, and monitoring progress toward goals over time. Impact Statement This article advances an understanding of the reliability and validity of a new multi-informant school climate assessment known as the New Jersey School Climate Improvement (NJ SCI) Survey. The survey can be used by school support staff including school psychologists, to collect data used to inform school climate improvement to create supportive and equitable learning conditions in schools.

18F]AlF-CBP imaging of type I collagen for non-invasive monitoring of pulmonary fibrosis in preclinical models.

Pulmonary fibrosis is an irreversible scar-forming condition for which there is a lack of non-invasive and specific methods for monitoring its progression and therapy efficacy. However, the disease is known to be accompanied by collagen accumulation. Here, we developed a novel positron emission tomography (PET) probe targeting type I collagen to evaluate its utility for the non-invasive assessment of pulmonary fibrosis. We designed a 18F-labeled PET probe ([18F]AlF-CBP) to target type I collagen and evaluated its binding affinity, specificity and stability in vitro. PET with [18F]AlF-CBP, CT, histopathology, immunofluorescence, and biochemical indice were performed to assess and quantify type I collagen levels and pulmonary fibrosis progression and treatment in murine models. Dynamic PET/CT studies of [18F]AlF-CBP were conducted to assess lung fibrosis in non-human primate models. [18F]AlF-CBP was successfully prepared, and in vitro and in vivo tests showed high stability (> 95%) and type I collagen specificity (IC50 = 0.36 µM). The lungs of the fibrotic murine model showed more elevated probe uptake and retention compared to the control group, and there was a positive correlation between the radioactivity uptake signals and the degree of fibrosis (CT: R2 = 0.89, P < 0.0001; hydroxyproline levels: R2 = 0.89, P < 0.0001). PET signals also correlated well with mean lung density in non-human primate models of pulmonary fibrosis (R2 = 0.84, P < 0.0001). [18F]AlF-CBP PET imaging is a promising non-invasive method for specific monitoring of lung fibrosis progression and therapy efficacy.

Serum glial fibrillary acidic protein as a marker of brain MRI metrics in multiple sclerosis: A scoping review.

Magnetic resonance imaging (MRI) is heavily relied upon for the diagnosis and monitoring of multiple sclerosis (MS), a chronic, demyelinating disease of the central nervous system. Serum biomarkers may serve as an accessible tool for increasing sensitivity, improving accessibility, corroborating symptoms, and providing additional data to guide clinical management. This scoping review investigates the current understanding of how the serum biomarker glial fibrillary acidic protein (sGFAP) relates to brain MRI metrics. We adhered to the Joanna Briggs Institute methodology for scoping reviews and the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. The databases Medline (Ovid), Embase (Ovid), CINAHL (Ebsco), and Web of Science (University of British Columbia institutional access) were searched on August 24, 2023 using a combination of medical subject headings and keyword terms for the topic of serum biomarkers in MS. A total of 9880 articles were retrieved in total of which 6271 unique titles and abstracts were screened. Twelve of the 259 resultant papers contained sGFAP data and proceeded to extraction. It was found that lesion MRI metrics generally had a positive relationship with sGFAP, while gray matter and white matter metrics, including normal-appearing white matter, were related negatively or not at all. These results highlight that while sGFAP may not be specific for MS, it may have utility for increasing sensitivity in postdiagnosis monitoring of MS progression.

Remote sensing reveals trends in vegetative recovery and land cover transformation post-reclamation at tar creek superfund site

The harmful effects of mining waste on human and ecosystem health make reclamation of former mine sites an environmental management priority. However, field-based monitoring of reclamation requires significant investments of time, labor, and money. Remote sensing offers a less expensive alternative to field-based monitoring, but work is still needed to determine which metrics can be reliably estimated using remote sensing techniques. This study uses remote sensing to examine over 20 years of reclamation efforts at Tar Creek Superfund Site and to assess revegetation after site restoration. Using the Normalized Difference Vegetation Index (NDVI), we quantify key factors affecting vegetation recovery and stability after reclamation across 123 surface mining waste cleanup locations within Tar Creek. Leveraging long-term imagery from Landsat and high-resolution PlanetScope imagery, we combine time series analysis of vegetation regrowth and landcover change detection for a comprehensive picture of recovery at each site. Across all reclamation sites, the average recovery duration was 3.5 years, and average recovery rate was 0.1 NDVI year−1. After vegetative growth had plateaued, reclaimed sites had an average NDVI of 0.70. All reclamation areas were converted from majority barren landcover to vegetated landcover classes after reclamation, and vegetative stability at reclamation sites was high (70% of reclaimed area saw continuous vegetative cover from 2017 to 2023). These results demonstrate a strong potential for multi-method remote sensing techniques in tracking and explaining vegetation recovery after reclamation and represent a cost-effective approach for real-time monitoring of reclamation progress and outcomes.

Developing and managing qualitative analysis protocol for a large‐scale, multisited project

AbstractCollaboration and communication serve as the foundation of team‐based research, and this extends to the analysis phase of a project. This article discusses the work involved in developing, communicating, and managing the analysis plan for a large‐scale, multisited project. To generate an effective analysis plan, we considered what data were needed to reach our project goals and then selected methods and designed data collection tools accordingly. Once data collection commenced, we developed a basic codebook that provided an overarching structure but flexibility for localized tailoring. We then coordinated updates to the coding scheme and conducted reliability checks on the analyses. All of this required regular communication and constant monitoring of progress toward project goals. While only one example, this article provides insights and guidelines for other researchers working on large, team‐based, and/or multisited qualitative projects.

Advancing autism therapy: emotion analysis using rehabilitation robots and ai for children with ASD

Emotion analysis is a key component in understanding the unique communication patterns and emotional states of children with autism spectrum disorder (ASD). These children often struggle with traditional forms of expressing emotions, which presents a challenge for themselves and their therapists. Facial expression analysis techniques, supported by modern technologies such as machine learning and artificial intelligence, enable more accurate identification of subtle signals that may go unnoticed by human observers. The introduction of rehabilitation robots and emotion analysis software based on the analysis of facial expressions and gestures opens up new possibilities for individualizing therapy, adapting it to the child's specific reactions and needs. In this way, the use of these tools not only increases the effectiveness of treatment but also helps build more trusting therapeutic relationships, which is the basis for adequate support for the development of children with ASD. Regular monitoring of progress and modifying therapeutic approaches, supported by automation and data analysis, is essential to more effective and empathetic care for children with developmental disorders. However, the journey does not end here. Further research is necessary to develop and improve emotion analysis techniques for use in rehabilitation robots and their impact on the effectiveness of therapy for young patients

Deciphering the role of HLF in idiopathic orbital inflammation: integrative analysis via bioinformatics and machine learning techniques

Idiopathic orbital inflammation, formerly known as NSOI (nonspecific orbital inflammation), is characterized as a spectrum disorder distinguished by the polymorphic infiltration of lymphoid tissue, presenting a complex and poorly understood etiology. Recent advancements have shed light on the HLF (Human lactoferrin), proposing its critical involvement in the regulation of hematopoiesis and the maintenance of innate mucosal immunity. This revelation has generated significant interest in exploring HLF's utility as a biomarker for NSOI, despite the existing gaps in our understanding of its biosynthetic pathways and operational mechanisms. Intersecting multi-omic datasets—specifically, common differentially expressed genes between GSE58331 and GSE105149 from the Gene Expression Omnibus and immune-related gene compendiums from the ImmPort database—we employed sophisticated analytical methodologies, including Lasso regression and support vector machine-recursive feature elimination, to identify HLF. Gene set enrichment analysis and gene set variation analysis disclosed significant immune pathway enrichment within gene sets linked to HLF. The intricate relationship between HLF expression and immunological processes was further dissected through the utilization of CIBERSORT and ESTIMATE algorithms, which assess characteristics of the immune microenvironment, highlighting a noteworthy association between increased HLF expression and enhanced immune cell infiltration. The expression levels of HLF were corroborated using data from the GSE58331 dataset, reinforcing the validity of our findings. Analysis of 218 HLF-related differentially expressed genes revealed statistically significant discrepancies. Fifteen hub genes were distilled using LASSO and SVM-RFE algorithms. Biological functions connected with HLF, such as leukocyte migration, ossification, and the negative regulation of immune processes, were illuminated. Immune cell analysis depicted a positive correlation between HLF and various cells, including resting mast cells, activated NK cells, plasma cells, and CD8 T cells. Conversely, a negative association was observed with gamma delta T cells, naive B cells, M0 and M1 macrophages, and activated mast cells. Diagnostic assessments of HLF in distinguishing NSOI showed promising accuracy. Our investigation delineates HLF as intricately associated with NSOI, casting light on novel biomarkers for diagnosis and progression monitoring of this perplexing condition.

Wildfire Threshold Detection and Progression Monitoring Using an Improved Radar Vegetation Index in California

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL’s UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire “burn” detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as “burned” compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance.

First insights of integrating the Bonn Internship Curriculum for Point-of-Care Ultrasound (BI-POCUS): progress and educational aspects

BackgroundPoint-of-care ultrasound (POCUS) is rapidly gaining prominence in various clinical settings. As its use becomes more widespread, there is a growing need for comprehensive ultrasound training in medical education to ensure that future healthcare professionals are proficient in this essential diagnostic tool.ObjectiveThis study is the first attempt by the University of Bonn to seamlessly integrate ultrasound courses and the use of ultrasound devices into the regular activities of final year medical students and to evaluate the usage of these devices.MethodsA total of forty students in their practical year were provided with a hendheld ultrasound device for a period of four months. During this time, they were invited to take part in eight optional ultrasound courses in which they acquired images and those images were rated using a specially developed rating system. At the end of the tertial, students were able to take part in a voluntary survey on the use of the equipment.ResultsParticipation in the optional ultrasound courses was well received, with the Introduction and FAST module drawing the largest number of participants (29). Among the ultrasound images acquired by students, those of the lungs obtaining the highest rating, with 18.82 (SD ± 4.30) points out of 23 points, while the aorta and vena cava images scored lowest, with an average of 16.62 (SD ± 1.55) points. The overall mean score for all images was 17.47 (SD ± 2.74). Only 21 students responded to the survey. Of the participating students, 67% used the device independently four times or fewer during the tertial.ConclusionThe study aimed to enhance the BI-POCUS curriculum by improving students' ultrasound skills during their practical year. However, device usage was lower than expected, with most students using it only once a month or less. This raises concerns about the justification of the effort and resources. Future initiatives will focus on technical improvements, better login data provision, and closer monitoring of usage and progress, emphasizing the need for practical ultrasound training in medical education.

Computer Vision-Based cybernetics systems for promoting modern poultry Farming: A critical review

As global demands on the poultry production and welfare both intensify, the precision poultry farming technologies such as computer vision-based cybernetics system is becoming important in addressing the current issues related to animal welfare and production efficiencies. The integration of computer vision technology has become a catalyst for transformative change in precision farming, particularly concerning productivity and welfare. This review paper delineates the central role of computer vision in precision poultry farming, focusing on its applications in non-contact monitoring methods that employ advanced sensors and cameras to enhance farm biosecurity and bird observation without disturbance. We delved into the multifaceted advancements such as the utilization of convolutional neural networks (CNNs) for behavior analysis and health monitoring, evidenced by the high accuracy sorting of eggs and identification of health concerns within target-dense farm environments. The review paper underscores advancements in precision agriculture, including accurate egg weight estimation and egg classification within cage-free systems, paralleling the poultry sector’s evolution towards more ethical farming practices. Moreover, it addresses the progress in poultry growth monitoring and examines case studies of commercial farms, showcasing how these innovations are being practically applied to enhance productivity and animal welfare. Challenges remain, particularly in terms of environmental variability and data annotation for deep learning models. Nevertheless, the review emphasizes the scope for future innovations like voice-controlled robotics and virtual reality applications, which have the potential to enhance poultry farming to new levels of efficiency, humanity, and sustainability. The insights assert that the continued exploration and development in computer vision technologies are not only instrumental for the poultry sector but also offer a blueprint for agricultural enhancement at large.