A global pandemic was declared by the World Health Organization for the coronavirus disease 2019, formerly known as 2019-nCoV (COVID-19), in March 2020. The burgeoning COVID patient count has triggered a crisis in the world's health infrastructure, making computer-aided diagnostics a crucial solution. A substantial portion of COVID-19 detection models using chest X-rays perform analysis at the image level. The infected region in the images is not recognized by these models, making a precise and accurate diagnosis challenging. Medical specialists can utilize lesion segmentation to precisely identify the infected areas in the lung. The current paper details a UNet-based encoder-decoder structure specifically designed for segmenting COVID-19 lesions observed in chest X-rays. The proposed model, aiming to enhance performance, leverages an attention mechanism and a convolution-based atrous spatial pyramid pooling module. The dice similarity coefficient and Jaccard index values for the proposed model were 0.8325 and 0.7132, respectively, representing an improvement over the benchmark UNet model. The contribution of the attention mechanism and small dilation rates within the atrous spatial pyramid pooling module was examined using an ablation study.
Recently, the detrimental and catastrophic impact of the COVID-19 infectious disease continues to have a pervasive global effect on human lives. To effectively address this devastating illness, prompt and cost-effective screening of afflicted individuals is crucial. Radiological examination remains the most practical approach to achieving this goal; however, readily available and affordable options include chest X-rays (CXRs) and computed tomography (CT) scans. Utilizing CXR and CT imagery, this paper introduces a novel ensemble deep learning approach to predict COVID-19 positive cases. This model aims to establish a highly effective COVID-19 prediction model, including a robust diagnostic approach and a significant increase in prediction accuracy. To prepare the input data for subsequent processing, pre-processing techniques like image resizing using scaling and noise removal using median filtering are initially applied. Applying data augmentation strategies, like flipping and rotation, allows the model to grasp the variability in the training data during training, resulting in superior outcomes with a smaller dataset. Ultimately, an innovative deep honey architecture (EDHA) model is developed for the purpose of successfully classifying COVID-19 cases into positive and negative categories. For the purpose of detecting the class value, EDHA combines the pre-trained models ShuffleNet, SqueezeNet, and DenseNet-201. The proposed model's hyper-parameter optimization within EDHA is achieved through the implementation of a new algorithm, the honey badger algorithm (HBA). The EDHA's implementation in Python is assessed by evaluating performance metrics such as accuracy, sensitivity, specificity, precision, F1-score, AUC, and Matthews correlation coefficient. The proposed model's capacity to function effectively was examined through the utilization of public CXR and CT datasets to evaluate the solution. Consequently, the simulated results demonstrated that the proposed EDHA outperformed existing techniques in terms of Accuracy, Sensitivity, Specificity, Precision, F1-Score, MCC, AUC, and Computational time, achieving 991%, 99%, 986%, 996%, 989%, 992%, 98%, and 820 seconds, respectively, using the CXR dataset.
A robust positive correlation is evident between the degradation of untouched natural landscapes and the surge in pandemics, consequently necessitating the deep scientific investigation of the zoonotic aspects. In contrast, containment and mitigation strategies form the core approach to halting a pandemic. The manner in which an infection spreads is of paramount significance during pandemics, and unfortunately, is often underestimated in the effort to combat deaths. From the Ebola outbreak to the unrelenting COVID-19 pandemic, the rise of recent pandemics emphasizes the need for deeper investigation into zoonotic transmission. A conceptual summary of the fundamental zoonotic mechanisms of the COVID-19 disease has been presented in this article, using available published data, and a schematic diagram of the transmission routes has been developed.
Motivated by discussions about the basic principles of systems thinking, Anishinabe and non-Indigenous scholars generated this paper. Probing the definition of 'system' through the question 'What is a system?', we encountered a substantial variation in our perspectives on its fundamental nature. oral pathology These divergent worldviews encountered by scholars operating in cross-cultural and inter-cultural contexts can cause systemic challenges in analyzing complex problems. Trans-systemics's language facilitates the discovery of these assumptions, acknowledging that the most prominent or forceful systems aren't always the most appropriate or equitable. Recognizing the interplay of multiple, overlapping systems and diverse worldviews is essential for effectively addressing intricate problems, surpassing the limitations of conventional critical systems thinking. DiR chemical research buy Indigenous trans-systemics, a critical lens for socio-ecological systems thinkers, yields three key insights: (1) it demands a posture of humility, compelling us to introspect and reassess our entrenched ways of thinking and acting; (2) embracing this humility, trans-systemics fosters a shift from the self-contained, Eurocentric systems paradigm to one acknowledging interconnectedness; and (3) applying Indigenous trans-systemics necessitates a fundamental re-evaluation of our understanding of systems, calling for the integration of diverse perspectives and external methodologies to effect meaningful systemic transformation.
Worldwide river basins are experiencing an increase in the frequency and severity of extreme events brought on by climate change. Efforts to develop resilience to these impacts are made difficult by the interwoven nature of social and ecological interactions, the multifaceted cross-scale influences, and the differing interests of diverse stakeholders, all of which influence the transformative dynamics in social-ecological systems (SESs). This study endeavored to explore the overarching patterns of a river basin under climate change by characterizing future conditions as the outcome of multifaceted interactions between various resilience initiatives and a complex, multi-scale socio-ecological system. The cross-impact balance (CIB) method, a semi-quantitative technique, served as the structure for a transdisciplinary scenario modeling process we facilitated. This process generated internally consistent narrative scenarios, drawing from a network of interacting drivers of change based on systems theory. Therefore, our study was also designed to examine the possibility of the CIB methodology unearthing varied viewpoints and forces that shape the evolution of SESs. This process was located in the Red River Basin, a transboundary water basin encompassing the United States and Canada, where natural climate fluctuations are amplified by the effects of climate change. The process yielded 15 interacting drivers, impacting agricultural markets and ecological integrity, leading to eight consistent scenarios that remain robust even with model uncertainty. The debrief workshop, alongside the scenario analysis, provides critical insights, including the required transformative changes for reaching desired outcomes and the cornerstone role of Indigenous water rights. In conclusion, our study exposed considerable intricacies related to building resilience, and underscored the capacity of the CIB approach to furnish unique perspectives on the evolution of SES systems.
The online version of the material includes supplementary resources, which can be found at 101007/s11625-023-01308-1.
Supplementary material for the online version is accessible at 101007/s11625-023-01308-1.
Across the globe, healthcare AI presents opportunities for transforming patient access, improving the quality of care provided, and ultimately, achieving better outcomes. This review promotes a more comprehensive and global approach in the development of healthcare AI solutions, with a particular emphasis on support for marginalized communities. Focusing specifically on medical applications, this review seeks to empower technologists with the knowledge and tools to build solutions in today's environment, understanding the obstacles that they face. This analysis delves into and examines the current obstacles in healthcare's foundational data and AI technology design, considering global implementation. We address the various factors that create a disparity in data availability, regulatory shortcomings for the healthcare industry, infrastructural challenges in power and network connectivity, and a lack of social support structures for healthcare and education, thereby limiting the potential universal effects of these technologies. The development of prototype healthcare AI solutions requires taking these considerations into account to better represent the needs of a global population.
The article highlights the key difficulties encountered in the process of crafting robotic ethics. The ethical considerations for robotics are multifaceted, including not only the consequences of their operation but also the ethical rules and principles robots must adhere to, a core component of Robotics Ethics. We advocate for the inclusion of the principle of nonmaleficence, often summarized as 'do no harm,' as a vital element in the ethical framework governing robots, especially those employed in healthcare settings. We submit, though, that the application of even this basic tenet will engender substantial difficulties for robot developers. In addition to the technical constraints, such as enabling robots to discern critical dangers and harmful situations in their environment, designers must determine a suitable field of responsibility for robots and specify which kinds of harm need to be prevented or avoided. These obstacles are intensified by the fact that the semi-autonomy of robots we currently design is unique from the semi-autonomy of more familiar entities like children or animals. fetal head biometry To summarize, robotic engineers are duty-bound to recognize and overcome significant ethical concerns in robotics before ethically deploying robots in the real world.