Research Days Poster: Cyber Threats on the Manufacturing Sector

Manufacturing companies are more vulnerable to internet attacks now that they are more reliant on technology and digital infrastructure. Cyberattacks on the manufacturing industry can have serious consequences, such as production delays, the theft of intellectual property, and damage to one\u27s reputation. At least 437 ransomware assaults occurred in the manufacturing sector in 2022, making up more than 70% of these expensive and disruptive attacks

BUILT IN SELF TEST FOR SAD MODULE IN MOTION ARRAY DETECTION

A novel method develops a built-in self-detection and correction (BISDC) architecture for motion estimation computing arrays(MECAs).Based on the error detection & correction concepts of biresidue codes, any single error in each processing element in an MECA can be effectively detected and corrected online using the proposed BISD and built-in selfcorrection circuits. Performance analysis and evaluation demonstrate that the proposed BISDC architecture performs well in error detection and correction with minor area i.e single error bit detection and correction . An advanced model has been proposed for multi bit detection using efficient adder implementation .a comparision is performed between efficient adder and processing element resultant

Teaching from the Edge: Transitioning into the Online Course Utilizing Instructional Design and Rubric Evaluation

Since 2006, the University of Kansas Libraries has offered a traditional, one credit class on information literacy research methods. In 2010, librarians began the process of revising this classroom-based course for online delivery in the spring 2012 semester. This presentation will walk attendees through the process of online course development of an information literacy class from its creation to completion. The presenters and co-instructors will discuss the shared responsibilities and challenges related to teaching an online course. The design of this online class will be broken down into different aspects related to development from a traditional library class course into an online only environment. The uses of instructional design concepts were an integral part of course development with the intention of enhancing student learning. The customization of learning objects was created using the SoftChalk software, online activities and video tutorials. The overall process of integrating these tools into the course management system, Blackboard, will also be detailed along with the challenges. The use of learning objectives and course activities, in particular, were utilized to address and assess weekly course milestones and to introduce and reinforce ARCL Information Literacy Standards as well as the learning goals (critical thinking, active learning) promoted on our campus. The presenters will discuss the course development process which included feedback from colleagues and the use of the Quality Matters™ Rubric a tool selected over other available rubrics to shape and guide our curriculum

Pattern of Crop Diversification in Tirunelveli district of Tamil Nadu

Crop diversification is considered a significant component for achieving higher output growth, higher farm income, employment generation, sustainability of natural resources and poverty alleviation. Understanding the pattern of crop diversification is important for improving agricultural production and for the well being of farmers. This paper analyses the pattern of crop diversification in Tirunelveli district of Tamil Nadu. A non – experimental research design was used for this study. A sample size of 120 respondents was fixed for the study using proportionate random sampling technique. It was found that the cropping pattern of crop diversification in the district has slightly been changed and had increased in two decades. It was also found that there was only slight variation in the categories of crop diversification index among the respondents from the years of 2007-2008 to 2017-2018. Significant steps have to be taken to improve the crop diversification among the farmers

Detection and prediction of monkey pox disease by enhanced convolutional neural network approach

Monkeypox is an infectious viral disease affecting both humans and animals. All symptoms are accompanied by a fever, swollen lymph nodes, and a rash that blisters before crusting. The interval between exposure and the development of symptoms is 5 to 21 days. Typically, symptoms last between two and four weeks. Although it is unknown to what degree it can happen without any signs. It has been found that not all outbreaks display the typical symptoms of fever, aches in the muscles, enlarged glands, and lesions appearing simultaneously. Cases may be severe, especially in children, pregnant women, or people with compromised immune systems. The problem can be detected and monitored at the early stages using some engineering solutions. Therefore, there is a necessity to develop accurate machine learning models for accurate interpretation before applying them in clinical trials. Hence, the proposed work has developed a model to diagnose monkey pox at the best accurate levels for accurate interpretation. The proposed enhanced convolutional neural network model is compared with the exisiting approaches. The obtained results were compared and indicate the superiority of the proposed algorithm

fNIRS complexity analysis for the assessment of motor imagery and mental arithmetic tasks

Conventional methods for analyzing functional near-infrared spectroscopy (fNIRS) signals primarily focus on characterizing linear dynamics of the underlying metabolic processes. Nevertheless, linear analysis may underrepresent the true physiological processes that fully characterizes the complex and nonlinear metabolic activity sustaining brain function. Although there have been recent attempts to characterize nonlinearities in fNIRS signals in various experimental protocols, to our knowledge there has yet to be a study that evaluates the utility of complex characterizations of fNIRS in comparison to standard methods, such as the mean value of hemoglobin. Thus, the aim of this study was to investigate the entropy of hemoglobin concentration time series obtained from fNIRS signals and perform a comparitive analysis with standard mean hemoglobin analysis of functional activation. Publicly available data from 29 subjects performing motor imagery and mental arithmetics tasks were exploited for the purpose of this study. The experimental results show that entropy analysis on fNIRS signals may potentially uncover meaningful activation areas that enrich and complement the set identified through a traditional linear analysis

In Vitro Tuberization and Quantitative Analysis of Colchicine Using Hptlc in Gloriosa Superba . L an Endangered Medicinal Plant of Pachamalai Hills, a Part of Eastern Ghats, Tamil Nadu.

Gloriosa superba. L has been a source of medicine right from ancient times. The tubers of this plant are sold in Indian herbal market as an important source of an alkaloid colchicine. Surface sterilized seeds of Gloriosa superba were soaked overnight in 1% GA3 on the next day seeds were planted on germinating media containing MS basal salts with 0.5 mg/l GA3 and 1.0 mg/l BA, 1% sucrose and 0.8% agar. 72.5% of seed germination was observed. The germinated seeds were transplanted on MS basal medium supplemented with 1.0 mg/l BAP, 0.05 mg/l GA3, 9.5 mg/l NAA and 6% sucrose which led to 90% tuber induction within 6 weeks of culture. Since there is a great demand of colchicine in the market, we have made an attempt to estimate the colchicine content in different parts of the plant like leaf, seed, pericarp, tuber and in vitro produced tuber using High Performance Thin Layer Chromatography, using a mixture of Ethyl acetate:Methanol (10:1.3 v/v) as mobile phase and precoated silica gel F254 TLC aluminium sheets as the stationary phase. The detection of spot was carried out at 350nm. The calibration curve was found to be linear between 100 to 600 ng/spot for colchicines. The results revealed that in vitro tuber had highest amount (0.14249%) of cochicine, followed by in vivo seed (0.10900%), tuber (0.05761%), leaves (0.46470%) and pericarp (0.04574%). The proposed method can be used to determine the colchicine content in Gloriosa superba

A Holistic Evaluation of Buddhism Literature: A Bibliometric Analysis of Global Publications Related to Buddhism Between 1975 and 2017

Although Buddhism is the fourth largest religion of the world with almost 500 million followers, to the best of our knowledge, academic literature lacks a bibliometric study investigating Buddhism documents. We used four databases provided by Web of Science; Thomson Reuters to extract the academic documents related to Buddhism and included all items published between 1975 and 2017. We generated info-maps and info-graphics showing distribution of world countries’ publication productivity and connections in bibliometric networks. A total of 25,267 articles were included and the most common document types were original articles, reviews and meeting reports (76.11, 19,38 and 3.84, respectively). English and Korean were the major languages of Buddhism literature (48.12 and 44.95%). United States of America (USA) was leading country with 4572 articles (18.81%) followed by the United Kingdom, China, Canada and Japan (3.32, 2.58, 2.1 and 2.07%, respectively). The most productive countries were Singapore, Australia, New Zealand and Taiwan (s = 19.34, 18.57, 16.42 and 15.45). We noted that six of ten most producing institutions in Buddhism literature were from the USA. No institutions from developing or least-developed countries were in the top-ten list. Researchers from the countries with large Buddhist population should be encouraged and supported to carry out more articles in Buddhist literature

Inferring directionality of coupled dynamical systems using Gaussian process priors: Application on neurovascular systems

Dynamical system theory has recently shown promise for uncovering causality and directionality in complex systems, particularly using the method of convergent cross mapping (CCM). In spite of its success in the literature, the presence of process noise raises concern about CCM’s ability to uncover coupling direction. Furthermore, CCM’s capacity to detect indirect causal links may be challenged in simulated unidrectionally coupled Rossler-Lorenz systems. To overcome these limitations, we propose a method that places a Gaussian process prior on a cross mapping function (named GP-CCM) to impose constraints on local state space neighborhood comparisons. Bayesian posterior likelihood and evidence ratio tests, as well as surrogate data analyses are performed to obtain a robust statistic for dynamical coupling directionality. We demonstrate GP-CCM’s performance with respect to CCM in synthetic data simulation as well as in empirical electroencephelography (EEG) and functional near infrared spectroscopy (fNIRS) activity data. Our findings show that GP-CCM provides a statistic that consistently reports indirect causal structures in non-separable unidirectional system interactions; GP-CCM also provides coupling direction estimates in noisy physiological signals, showing that EEG likely causes, i.e., drives, fNIRS dynamics