Search results for: Lakshmidevi Rajendran

Authors: Lakshmidevi Rajendran, Bharathidasan Kanniappan, Gopi Raja, Muthukumar Karuppan

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This study investigates the feasibility of fed-batch mixotrophic cultivation of microalgae Scenedesmus sp. in a 3-litre airlift photobioreactor under standard operating conditions. The results of this study suggest the algae species may serve as an excellent feed for aquatic species using organic byproducts. Microalgae Scenedesmus sp., was cultured using a synthetic wastewater by stepwise addition of crude glycerol concentration ranging from 2-10g/l under fed-batch mixotrophic mode for a period of 15 days. The attempts were made with the stepwise addition of crude glycerol as a carbon source in the initial growth phase to evade the inhibitory nature of high glycerol concentration on the growth of Scenedesmus sp. Crude glycerol was chosen since it is readily accessible as byproduct from biodiesel production sectors. Highest biomass concentration was achieved to be 2.43 g/l at the crude glycerol concentration of 6g/l after 10 days which is 3 fold times the increase in the biomass concentration compared with the control medium without the addition of glycerol. Biomass growth data obtained for the microalgae Scenedesmus sp. was fitted well with the modified Logistic equation. Substrate utilization kinetics was also employed to model the biomass productivity with respect to the various crude glycerol concentration. The results indicated that the supplement of crude glycerol to the mixotrophic culture of Scenedesmus sp., enhances the biomass concentration, chlorophyll and lutein productivity. Thus the application of fed-batch mixotrophic cultivation with stepwise addition of crude glycerol to Scenedesmus sp., provides a subtle way to reduce the production cost and improvisation in the large-scale cultivation along with biochemical compound synthesis.

Keywords: airlift photobioreactor, crude glycerol, microalgae Scenedesmus sp., mixotrophic cultivation, lutein production

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Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

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For security purposes, it is important to detect passwords entered by unauthorized users. With traditional alphanumeric passwords, if the content of a password is acquired and correctly entered by an intruder, it is impossible to differentiate the password entered by the intruder from those entered by the authorized user because the password entries contain precisely the same character set. However, no two entries for the gesture-based passwords, even those entered by the person who created the password, will be identical. There are always variations between entries, such as the shape and length of each stroke, the location of each stroke, and the speed of drawing. It is possible that passwords entered by the unauthorized user contain higher levels of variations when compared with those entered by the authorized user (the creator). The difference in the levels of variations may provide cues to detect unauthorized entries. To test this hypothesis, we designed an empirical study, collected and analyzed the data with the help of machine-learning algorithms. The results of the study are significant.

Keywords: authentication, gesture-based passwords, shoulder-surfing attacks, usability

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: Sankaran Rajendran

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Remote sensing plays a vital role in mapping of resources and monitoring of environments of the earth. In the present research study, mapping and monitoring of clay siltations occurred in the Alkhod Dam of Muscat, Sultanate of Oman are carried out using low-cost multispectral Landsat and ASTER data. The dam is constructed across the Wadi Samail catchment for ground water recharge. The occurrence and spatial distribution of siltations in the dam are studied with five years of interval from the year 1987 of construction to 2014. The deposits are mainly due to the clay, sand, and silt occurrences derived from the weathering rocks of ophiolite sequences occurred in the Wadi Samail catchment. The occurrences of clays are confirmed by minerals identification using ASTER VNIR-SWIR spectral bands and Spectral Angle Mapper supervised image processing method. The presence of clays and their spatial distribution are verified in the field. The study recommends the technique and the low-cost satellite data to similar region of the world.

Keywords: Alkhod Dam, ASTER siltation, Landsat, remote sensing, Oman

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Authors: Ranjeeta Kumari, Makesh M, Gayatri Tripathi, K V Rajendran, Megha Bedekar

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A comparative study on DNA immunization with recombinant glyceraldehyde-3-phosphate dehydrogenase (GAPDH) construct of Edwardsiella tarda (pGPD group) and a bicistronic construct expressing GAPDH plus IFN-γ of Labeo rohita as adjuvant (pGPD+IFN group) was undertaken in Labeo rohita along with the control animals. Successful co-expression of two genes that is GAPDH and IFN-γ was confirmed in SSN-1 cells line by RT-qPCR and western blot. The protective immune response of host to DNA vaccine construct was determined by RPS and specific antibody production. Fishes immunized with plasmids via intramuscular injection (I/M) exhibited a considerable relative percentage survivability of 66.66% in pGPD+IFN immunized group and 53.34% in pGPD immunized group after challenge with E. tarda. Antibody response was also significantly high in pGPD+IFN group at all time points under study. This was analysed by competitive ELISA, using anti GAPDH monoclonal antibodies. The experiment revealed that the GAPDH gene of E. tarda is one of the ideal candidates for generating protective immune response in L. rohita. Further addition of Interferon gamma to DNA vaccine construct can enhance the immune response in host.

Keywords: DNA vaccine, Edwardsiella tarda, Labeo rohita, zoonosis, immune response

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Authors: Bahare Askarian, Suchithra Rajendran

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Maintaining the freshness and quality of perishable goods during distribution is a critical challenge for logistics companies. This study presents a comprehensive framework aimed at optimizing the distribution of perishable goods through a mathematical model of the Transportation Inventory Location Routing Problem (TILRP). The model incorporates the impact of product age on customer demand, addressing the complexities associated with inventory management and routing. To tackle this problem, we develop both simple and hybrid metaheuristic algorithms designed for small- and medium-scale scenarios. The hybrid algorithm combines Biogeographical Based Optimization (BBO) algorithms with local search techniques to enhance performance in small- and medium-scale scenarios, extending our approach to larger-scale challenges. Through extensive numerical simulations and sensitivity analyses across various scenarios, the performance of the proposed algorithms is evaluated, assessing their effectiveness in achieving optimal solutions. The results demonstrate that our algorithms significantly enhance distribution efficiency, offering valuable insights for logistics companies striving to improve their perishable goods supply chains.

Keywords: perishable goods, meta-heuristic algorithm, vehicle problem, inventory models

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Authors: Maniyarasan Rajendran

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Every design has a process, and every architect deals in the ways best known to them. The design translation from the concept to completion change in accordance with their design philosophies, their tools, availability of resources, and at times the clients and the context of the design as well. The approach to understanding the design process requires formalisation of the design intents. The design process is characterised by change, with the time and the technology. The design flow is just indicative and never exhaustive. The knowledge and experience of stakeholders remain limited to the part they played in the project, and their ability to remember, and is through the Photographs. These artefacts, when circulated can hardly tell what the project is. They can never tell the narrative behind. In due course, the design processes are lost. The Design junctions are lost in the journey. Photographs acted as major source materials, along with its importance in architectural revivalism in the 19th century. From the history, we understand that it has been photographs, that act as the dominant source of evidence. The idea of recording is also followed with the idea of getting inspired from the records and documents. The design concept, the architectural firms’ philosophies, the materials used, the special needs, the numerous ‘Trial-and-error’ methods, design methodology, experience of failures and success levels, and the knowledge acquired, etc., and the various other aspects and methods go through in every project, and they deserve/ought to be recorded. The knowledge can be preserved and passed through generations, by documenting the design processes involved. This paper explores the idea of a process documentation as a tool of self-reflection, creation of architectural firm’ repository, and these implications proceed with the design evolution of the team.

Keywords: architecture, design, documentation, records

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Authors: Swajeeth Panchangam, Arun Rajendran, Swarnim Gupta, Ahmed Zeouita

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Semiconductor processing chambers tend to operate in controlled but aggressive operating conditions (chemistry, plasma, high temperature etc.) Owing to this, the design of this equipment requires developing robust and reliable hardware and software. Any equipment downtime due to reliability issues can have cost implications both for customers in terms of tool downtime (reduced throughput) and for equipment manufacturers in terms of high warranty costs and customer trust deficit. A thorough reliability assessment of critical parts and a plan for preventive maintenance/replacement schedules need to be done before tool shipment. This helps to save significant warranty costs and tool downtimes in the field. However, designing a proper reliability test plan to accurately demonstrate reliability targets with proper sample size and test duration is quite challenging. This is mainly because components can fail in different failure modes that fit into different Weibull beta value distributions. Without apriori Weibull beta of a failure mode under consideration, it always leads to over/under utilization of resources, which eventually end up in false positives or false negatives estimates. This paper proposes a methodology to design a reliability test plan with optimal model size/duration/both (independent of apriori Weibull beta). This methodology can be used in demonstration tests and can be extended to accelerated life tests to further decrease sample size/test duration.

Keywords: reliability, stochastics, preventive maintenance

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Authors: Jayanthi Rajendran

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Experience is what makes a man perfect. Though we tend to learn many a different things in life through practice still we need to go an extra mile to gain experience which would be profitable only when it is integrated with regular practice. A clear phenomenal idea is that every teacher is a learner. The centralized idea of this paper would focus on the integrated practices carried out among the students of Jizan University which enhances learning through experiences. Integrated practices like student-directed activities, balanced curriculum, phonological based activities and use of consistent language would enlarge the vision and mission of students to earn experience through learning. Students who receive explicit instruction and guidance could practice the skills and strategies through student-directed activities such as peer tutoring and cooperative learning. The second effective practice is to use consistent language. Consistent language provides students a model for talking about the new concepts which also enables them to communicate without hindrances. Phonological awareness is an important early reading skill for all students. Students generally have phonemic awareness in their home language can often transfer that knowledge to a second language. And also a balanced curriculum requires instruction in all the elements of reading. Reading is the most effective skill when both basic and higher-order skills are included on a daily basis. Computer based reading and listening skills will empower students to understand a language in a better way. English language learners can benefit from sound reading instruction even before they are fully proficient in English as long as the instruction is comprehensible. Thus, if students have to be well equipped in learning they should foreground themselves in various integrated practices through multifarious experience for which teachers are moderators and trainers. This type of learning prepares the students for a constantly changing society which helps them to meet the competitive world around them for better employability fulfilling the vision and mission of the institution.

Keywords: consistent language, employability, phonological awareness, balanced curriculum

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Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

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Graphical-based passwords have existed for decades. Their major advantage is that they are easier to remember than an alphanumeric password. However, their disadvantage (especially recognition-based passwords) is the smaller password space, making them more vulnerable to brute force attacks. Graphical passwords are also highly susceptible to the shoulder-surfing effect. The gesture-based password method that we developed is a grid-free, template-free method. In this study, we evaluated the gesture-based passwords for usability and vulnerability. The results of the study are significant. We developed a gesture-based password application for data collection. Two modes of data collection were used: Creation mode and Replication mode. In creation mode (Session 1), users were asked to create six different passwords and reenter each password five times. In replication mode, users saw a password image created by some other user for a fixed duration of time. Three different duration timers, such as 5 seconds (Session 2), 10 seconds (Session 3), and 15 seconds (Session 4), were used to mimic the shoulder-surfing attack. After the timer expired, the password image was removed, and users were asked to replicate the password. There were 74, 57, 50, and 44 users participated in Session 1, Session 2, Session 3, and Session 4 respectfully. In this study, the machine learning algorithms have been applied to determine whether the person is a genuine user or an imposter based on the password entered. Five different machine learning algorithms were deployed to compare the performance in user authentication: namely, Decision Trees, Linear Discriminant Analysis, Naive Bayes Classifier, Support Vector Machines (SVMs) with Gaussian Radial Basis Kernel function, and K-Nearest Neighbor. Gesture-based password features vary from one entry to the next. It is difficult to distinguish between a creator and an intruder for authentication. For each password entered by the user, four features were extracted: password score, password length, password speed, and password size. All four features were normalized before being fed to a classifier. Three different classifiers were trained using data from all four sessions. Classifiers A, B, and C were trained and tested using data from the password creation session and the password replication with a timer of 5 seconds, 10 seconds, and 15 seconds, respectively. The classification accuracies for Classifier A using five ML algorithms are 72.5%, 71.3%, 71.9%, 74.4%, and 72.9%, respectively. The classification accuracies for Classifier B using five ML algorithms are 69.7%, 67.9%, 70.2%, 73.8%, and 71.2%, respectively. The classification accuracies for Classifier C using five ML algorithms are 68.1%, 64.9%, 68.4%, 71.5%, and 69.8%, respectively. SVMs with Gaussian Radial Basis Kernel outperform other ML algorithms for gesture-based password authentication. Results confirm that the shorter the duration of the shoulder-surfing attack, the higher the authentication accuracy. In conclusion, behavioral features extracted from the gesture-based passwords lead to less vulnerable user authentication.

Keywords: authentication, gesture-based passwords, machine learning algorithms, shoulder-surfing attacks, usability

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Authors: Keshav Dubey, Swajeeth Panchangam, Arun Rajendran, Swarnim Gupta

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In the semiconductor capital equipment industry, effective modeling of repairable system reliability is crucial for optimizing maintenance strategies and ensuring operational efficiency. However, repairable system reliability modeling using a renewal process is not as popular in the semiconductor equipment industry as it is in the locomotive and automotive industries. Utilization of this approach will help optimize maintenance practices. This paper presents a structured framework that leverages both parametric and non-parametric approaches to model the reliability of repairable subsystems based on operational data, maintenance schedules, and system-specific conditions. Data is organized at the equipment ID level, facilitating trend testing to uncover failure patterns and system degradation over time. For non-parametric modeling, the Mean Cumulative Function (Mean Cumulative Function) approach is applied, offering a flexible method to estimate the cumulative number of failures over time without assuming an underlying statistical distribution. This allows for empirical insights into subsystem failure behavior based on historical data. On the parametric side, virtual age modeling, along with Homogeneous and Non-Homogeneous Poisson Process (Homogeneous Poisson Process and Non-Homogeneous Poisson Process) models, is employed to quantify the effect of repairs and the aging process on subsystem reliability. These models allow for a more structured analysis by characterizing repair effectiveness and system wear-out trends over time. A comparison of various Generalized Renewal Process (GRP) approaches highlights their utility in modeling different repair effectiveness scenarios. These approaches provide a robust framework for assessing the impact of maintenance actions on system performance and reliability. By integrating both parametric and non-parametric methods, this framework offers a comprehensive toolset for reliability engineers to better understand equipment behavior, assess the effectiveness of maintenance activities, and make data-driven decisions that enhance system availability and operational performance in semiconductor fabrication facilities.

Keywords: reliability, maintainability, homegenous poission process, repairable system

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