From Dialectical Closure to ParallacticalIndeterminacy: A Study of the Political and Individual Modes of Being in Slavoj Zizek’s Antigone

Parallax is the difference in perception caused by the spatial shift of the observer and the observed. Slovenian philosopher Slavoj Zizek has used thisscientific notion to interpret apparently antithetical positions in the fields of politics, neurobiologyand philosophy. His contention is that the parallax shift makes some phenomenon appear as two, while a change in perspective can make us see that theyare, in fact, ONE. The notion of parallax can also be exploited to read a literary text and, in this article, I intend to use it to read Slavoj Zizek’s own re-writing of Sophocles’ play Antigone. Antigone, as a character, has enamored and appalled criticsand philosophers throughout history. Her defiance against the State has been interpreted and evaluated from different perspectives and viewpoints. The play stages the conflict between two modes of being, thepoliticaland the individual,and the appeal ofits polemic seems not to have gotten stale all these centuries. In his re-writing, Slavoj Zizek has provided two alternates to the original ending. He has described this as an “ethico-political exercise”(Zizek,2016, p. xxv)and not a literary venture butI have attempted to read his play as a literary text applying his philosophical notion of the parallax. I have used textual analysis as my method in order to read the selected text. My contention is that the two alternate endings provided by Zizek presentthe individual and political as two warring modes of being but a shift in parallactical position can make them appear as ONE. Moreover, it can also be argued that even the two alternates are an outcome of a parallactical movement in perspective that masksthe inherent ONENESS of the two

Learning Organizational Practices and Job Satisfaction: A Case of IT Sector of Karachi

With the increase of globalization and technological advancements since last few decades, organizations have gone through several changes in terms of workplace diversity, learning differentiation as well as organizational culture. This study aims to test the relationship between Learning Organization and Job Satisfaction in the IT Sector of Karachi. Data were collected from 209 such employees of the IT sector who have been working for the same organization for at-least one year. Multiple Regression Analysis was used as the statistical technique. It was found that two of the dimensions of learning organizational practices namely continuous learning and strategic leadership have significant impact on job satisfaction in the IT Sector of Karachi. Hence, it is suggested that managers in the IT Sector of Karachi should design the jobs in such a manner that employees be able to learn continuously. Furthermore, it is suggested that researchers should study the learning organizational practices in relation to other work outcomes such as employee engagement and the turnover intentions

CartiMorph: a framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient $\rho \in [0.82,0.97]$), surface area ($\rho \in [0.82,0.98]$) and volume ($\rho \in [0.89,0.98]$) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.Comment: To be published in Medical Image Analysi

AI in drug discovery and its clinical relevance

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.  Other InformationPublished in:HeliyonLicense: https://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://doi.org/10.1016/j.heliyon.2023.e17575 </p

Multi-objective optimization of process parameters during micro-milling of nickel-based alloy Inconel 718 using Taguchi-grey relation integrated approach

This research investigates the machinability of Inconel 718 under conventional machining speeds using three different tool coatings in comparison with uncoated tool during milling operation. Cutting speed, feed rate and depth of cut were selected as variable machining parameters to analyze output responses including surface roughness, burr formation and tool wear. It was found that uncoated and AlTiN coated tools resulted in lower tool wear than nACo and TiSiN coated tools. On the other hand, TiSiN coated tools resulted in highest surface roughness and burr formation. Among the three machining parameters, feed was identified as the most influential parameter affecting burr formation. Grey relational analysis identified the most optimal experimental run with a speed of 14 m/min, feed of 1 mu m/tooth, and depth of cut of 70 mu m using an AlTiN coated tool. ANOVA of the regression model identified the tool coating parameter as most effective, with a contribution ratio of 41.64%, whereas cutting speed and depth of cut were found to have contribution ratios of 18.82% and 8.10%, respectively. Experimental run at response surface optimized conditions resulted in reduced surface roughness and tool wear by 18% and 20%, respectively.Web of Science1523art. no. 829

Performance Assessment and Working Fluid Selection for Novel Integrated Vapor Compression Cycle and Organic Rankine Cycle for Ultra Low Grade Waste Heat Recovery

This paper presents the performance assessment and working fluid selection for a novel integrated vapor compression cycle-organic Rankine cycle system (i-VCC-ORC), which recovers ultra-low-temperature waste heat rejected (50 °C) by the condenser of a vapor compression cycle (VCC). The analyses are carried out for a vapor compression cycle of a refrigeration capacity (heat input) of 35kW along with the component sizing of the organic Rankine cycle (ORC). The effects of the operational parameters on integrated system performance were investigated. The integrated system performance is estimated in terms of net COP, cycle thermal efficiency and exergy efficiency by completely utilizing and recovering the heat rejected by the condenser of the VCC system. R600a-R141b with COPnet (3.54) and ORC thermal efficiency (3.05%) is found to be the most suitable VCC-ORC working fluid pair. The integration of the vapor compression refrigeration cycle with the organic Rankine cycle increases the COP of the system by 12.5% as compared to the standalone COP of the vapor compression system. Moreover, the sensitivity analysis results show that there exists an optimum operating condition that maximizes the thermal performance of the integrated system

Traffic engineering and multiprotocol label switching as mean to improve network efficiency

Multiprotocol label switching (MPLS) is an emerging technology that provides scalability, flexibility and use the available bandwidth in the network in an efficient way. Signaling protocols like constraint based routing; the label distribution protocol and the resource reservation protocol can be used to enable good traffic engineering. Interior gateway protocols work in conjunction with signaling protocols and their strong binding result in better performance of the network.   In this thesis, we have analyzed the performance of the signaling protocols used in the MPLS paradigm for traffic engineering. These signaling protocols are meant to provide support for traffic engineering using MPLS and in this way help to increase the performance of the network. Some issues related to increase the efficiency of the signaling protocols are scrutinized. How the resource reservation protocol has been extended to support traffic engineering in multi protocol label switching paradigm is also discussed. Moreover, application of multiprotocol label switching to traffic engineering is implemented in a proposed network

Learning Organizational Practices and Job Satisfaction: A Case of IT Sector of Karachi

With the increase of globalization and technological advancements since last few decades, organizations have gone through several changes in terms of workplace diversity, learning differentiation as well as organizational culture. This study aims to test the relationship between Learning Organization and Job Satisfaction in the IT Sector of Karachi. Data were collected from 209 such employees of the IT sector who have been working for the same organization for at-least one year. Multiple Regression Analysis was used as the statistical technique. It was found that two of the dimensions of learning organizational practices namely continuous learning and strategic leadership have significant impact on job satisfaction in the IT Sector of Karachi. Hence, it is suggested that managers in the IT Sector of Karachi should design the jobs in such a manner that employees be able to learn continuously. Furthermore, it is suggested that researchers should study the learning organizational practices in relation to other work outcomes such as employee engagement and the turnover intentions

Human Expression Recognition using Facial Shape Based Fourier Descriptors Fusion

Dynamic facial expression recognition has many useful applications in social networks, multimedia content analysis, security systems and others. This challenging process must be done under recurrent problems of image illumination and low resolution which changes at partial occlusions. This paper aims to produce a new facial expression recognition method based on the changes in the facial muscles. The geometric features are used to specify the facial regions i.e., mouth, eyes, and nose. The generic Fourier shape descriptor in conjunction with elliptic Fourier shape descriptor is used as an attribute to represent different emotions under frequency spectrum features. Afterwards a multi-class support vector machine is applied for classification of seven human expression. The statistical analysis showed our approach obtained overall competent recognition using 5-fold cross validation with high accuracy on well-known facial expression dataset