From February 2020, the COVID-19 pandemic led to closures of educational institutions to reduce the spread of infectious disease. This forced the U.S. education system into a massive experiment with online education. Despite conducting online bioethics education for nearly twenty years, our bioethics program, a joint endeavor of Clarkson University and Icahn School of Medicine at Mount Sinai, was not immune to this disruption because our curriculum features intensive, one-week onsite courses. Even in the face of historic disruptions, it is (...) vital to ensure minimal interruptions to teaching and assessing students to provide effective education. This paper reviews the steps we took to successfully convert the onsite components of our curriculum to a synchronous online format, and it focuses on how we preserved instruction and assessment of practical skills that comprise these courses’ core. It also explains how we fostered interactive classroom environments. (shrink)

Sustainable development is a key notion in today’s business world. More frequently, Sustainability is thought to be solely dependent on resource management and production processes. However, HRM (Human Resource Management) plays an important role in its actual implementation. This paper focuses on the growing importance of social sustainability aspects of sustainable HRM that contribute to the sustainability of the organizations. The study identified ten significant social sustainability factors of sustainable HRM through a comprehensive literature review and experts. The ‘Total Interpretive (...) Structural Modelling (TISM) technique was used to uncover the relationship between the indicated factors and to establish their structural hierarchy. Further MICMAC (Matriced Impact Croises Multiplication Applique) study was performed to assess each factor’s driving-dependence power. The factor, namely “availability of equal employment opportunities” was found to be the most important factor, whereas “social trust” was observed to be highly dependent on the remaining factors. The intended framework might facilitate practitioners and decision-makers to understand the interrelationships among the social sustainability factors of sustainable HRM. This will be useful for SME owners and managers in comprehending the opportunities in improving organizational performance via social sustainability factors. (shrink)

The concept of free will states that when more than one alternative is available to an individual, he/she chooses freely and voluntarily to render an action in any given context. A question arises, how do human beings choose to perform an action in a given context? What happens to an individual who compels him/her to choose an action out of many alternatives? The behaviorists state that free will guides individuals to choose an action voluntarily. Therefore, he/she is morally responsible for (...) his/her voluntary actions. This paper attempts to answer whether a person’s action is an outcome of his/her ‘moral luck’ or ‘designated cerebral cortex processes’? While answering this question the paper examines the relation between principle of causality and free will of a person. It analyses what causes free will. It elucidates use of free will for decision-making process(es), and the relation between human beings’ actions and their moral responsibility. This paper concludes that human beings are conscious of their free will. Free will decides their actions; therefore, they are morally responsible for their voluntary actions. This paper argues that free will is an emergent property of ‘immaterial soul’. (shrink)

Advances in intracranial electroencephalography and neurophysiology have enabled the study of previously inaccessible brain regions with high fidelity temporal and spatial resolution. Studies of iEEG have revealed a rich neural code subserving healthy brain function and which fails in disease states. Machine learning, a form of artificial intelligence, is a modern tool that may be able to better decode complex neural signals and enhance interpretation of these data. To date, a number of publications have applied ML to iEEG, but clinician (...) awareness of these techniques and their relevance to neurosurgery, has been limited. The present work presents a review of existing applications of ML techniques in iEEG data, discusses the relative merits and limitations of the various approaches, and examines potential avenues for clinical translation in neurosurgery. One-hundred-seven articles examining artificial intelligence applications to iEEG were identified from 3 databases. Clinical applications of ML from these articles were categorized into 4 domains: i) seizure analysis, ii) motor tasks, iii) cognitive assessment, and iv) sleep staging. The review revealed that supervised algorithms were most commonly used across studies and often leveraged publicly available timeseries datasets. We conclude with recommendations for future work and potential clinical applications. (shrink)

Histopathological lung cancer segmentation using region of interest is one of the emerging research area in the field of health monitoring system. In this paper, the histopathological images were collected from the database Stanford Tissue Microarray Database (TMAD). After image collection, pre-processing was performed using a normalization technique, which enhances the quality of the histopathological image by eliminating unwanted noise. After pre-processing, segmentation was carried out using the modified kernel-based fuzzy c-means clustering (KFCM) approach along with the edge bridge and (...) fill technique (EBFT). It was a flexible high-level machine learning technique to localize the object in a complex template. The experimental result shows that the proposed approach segments the normal and abnormal cancer regions by means of precision, recall, specificity, accuracy, and Jaccard coefficient. The proposed methodology improved the classification accuracy in lung cancer segmentation up to 2.5–5% compared to the existing methods deep convolutional neural network (DCNN) and diffusion-weighted approach. (shrink)

. The problem of reconstructing digital images from degraded measurements is regarded as a problem of importance in various fields of engineering and imaging science. The main goal of denoising is to restore a noisy image to produce a visually high quality image. In this paper, we propose a novel transform domain technique that uses multispinning for image denoising. The proposed method uses multiple cyclic shifted versions of an image, where each of them would capture more detail information during decomposition. (...) Discrete wavelet transform and contourlet transform in association with multispinning is used. The results are compared with traditional transform and spatial domain techniques. The visual and quantitative evaluation suggests that the proposed method yields better results. (shrink)