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Performance investigation of PID controller in trajectory control of two-link robotic manipulator in medical robots
Robot-assisted surgical procedures have gained much coverage in recent years and favored over manually conducted operations. The medical robots are comprised of manipulators arm that is the multi-degree of freedom positioning devices with a highly non-linear nature to perform various surgical tasks. Due to non-linear effects, robots offer a severe challenge to the control system. Therefore, the control techniques are required for controlling the robots that should be fast enough to accommodate the rapid changes in the system parameters. In this article, the Proportional-Integral-Derivative (PID) controllers performance has been investigated in trajectory control of the Two-Link Robotic Manipulator (TLRM) for reliable functioning of these robots. Tracking error and Control input factors have been used to investigate the PID controllers robustness in trajectory control of TLRM. Eulers-lagrange approach has been used for dynamic analysis of TLRM. This work has been accomplished in the MATLAB/ Simulink environment. 2021 Taru Publications. -
Multidisciplinary Perspectives in Social Cognition: Recent Advances and Future Questions
This book introduces social cognition from multidisciplinary perspectives, exploring in detail how individuals perceive, interpret, and respond to social stimuli throughout different stages of life. The book is organized into five parts, and each chapter provides a concise introduction to a specific topic within social cognition, followed by an in-depth exploration of prominent areas of research, the impact of technology, and potential future questions. The book covers core principles of social cognition alongside application in clinical interventions, with topics including how social cognition evolves throughout the lifespan, the impact of technology and social media on social cognition, and how conditions like autism, schizophrenia, and dementia affect social processing. It links social cognition with critical cognitive domains, including attention, memory, and decision-making, highlighting how these processes interact in shaping social behaviour. The book also combines cross-cultural research with relevant studies to show the differences and similarities in social cognition across geographical distances. Each part concludes with guided questions and answers for students to test their understanding, knowledge, and learning outcomes. By synthesizing current research and identifying future directions, this book offers valuable insights and potential solutions to the complex challenges in the field of social cognition. Bringing together foundational concepts with advances in the field, the book is ideal reading for students of social psychology, cognitive psychology, social neuroscience, and neuropsychology. It will also be of interest to students and researchers of clinical psychology, providing a foundation for future research in applied settings. 2025 K. Jayasankara Reddy and Bhasker Malu. -
Isolation of Fungal Endophytes From Hulimavu Lake Flora and Characterization and Optimization of Fungal Enzymes
The present work was aimed at isolating fungal endophytes from Hulimavu Lake, newlineextracting their secondary metabolites and fungal enzymes to subsequently probe the newlinebiological properties of these fungal endophytic bioactive compounds and enzymes. newlineHulimavu Lake, known for its notoriously high levels of pollution contributed by newlineanthropogenic factors, was chosen for this study with the literature-evidence backed newlinehypothesis that plant habitat stress also positively favors the type and quanta of bioactive compounds with novel features produced by its endophytes. Literature survey was performed to identify the probable types of plants found in this lake that could be used for the study and these were subsequently collected, identified and processed under laboratory conditions. These plants were authenticated by a certified botanist and were identified as Alternanthera philoxeroides, Ricinus communis and Persicaria glabra. Fungal endophytes were then isolated from different plant parts collected and were screened using preliminary LCB staining followed by DNA sequencing analysis. Based on ITS region sequencing, nucleotide homology and phylogenetic tree mapping, these fungal endophytic cultures were identified as Aspergillus niger, Talaromyces amestolkiae, Cladosporium phaseolorum and Diaporthe phaseolorum. Crude extracts obtained from these fungal endophytes displayed bacterial growth inhibition and significant free radical scavenging/ reduction potential that was comparable to standard ascorbic acid, hence depicting antioxidant activity of these extracts. Owing to their biological properties these crude extracts were further tested for cytotoxic properties on newlinedifferent models like that of Saccharomyces cerevisiae (Baker s yeast), Artemia salina newline(Brine shrimp) and MCF-7 cell line. The presence of fungal endophytic enzymes like newlineprotease, amylase, laccase and lipase was detected qualitatively and estimated newlinequantitatively. -
Bivariate Cointegrated Model with Gamma Innovations
The nature of time-bound data is its non-stationarity, that is, the constant presence of factors such as trend, seasonality, or both. Adopting mechanisms such as the method of differencing or ordinary least squares results in a loss of information or overestimation or underestimation of the parameters, respectively. A cointegration study reflects the notion of a long-run equilibrium, which is a concept of sensitivity in macroecometrics. Thus, cointegration can be defined as the onset of a longterm equilibrium between two or more time series that evolve under the influence of time, with the potential advantage of establishing a dynamic relationship using standard methods. Thus, this study explores the theoretical approach of estimating an error correction model for a cointegrated bivariate VAR (2) model with gamma innovation. To obtain the parameter estimates of the proposed model, we employ the conditional maximum likelihood estimation, implemented through the NewtonRaphson algorithm, because of the gamma distributions non-closed form nature. A theoretical study is strengthened by artificial simulations that support mathematical derivations. 2025 Scrivener Publishing LLC. -
Citrus for wellness: Exploring the bioactive properties of Citrus medica fruit peel with emphasis on its anticancer, antioxidant, antimicrobial and anthelmintic properties
Citrus medica (Citron) is an underutilised plant consisting of various bioactive elements with numerous medicinal benefits. The present study aimed to evaluate the bioactive properties, including anthelmintic, antimicrobial, antioxidant and anticancer activities, of chloroform extract obtained from the of fruit peel of C. medica. The different types of phytochemicals present in the chloroform extract were analysed using GC-MS. The major components detected included n-hexadecanoic acid, octadecanoic acid, t-tetradecenal, 1-nonadecene etc. Anthelmintic study was conducted using Eisenia fetida as a test organism, revealing a significant anthelmintic effect in the C. medica fruit peel extract compared to the standard drug. Antimicrobial properties were assessed against five test bacterial and fungal strains. Antibacterial tests showed zones of inhibition ranging from 8 to 11 mm, while no prominent zones of inhibition were observed in antifungal tests. The DPPH assay demonstrated significant antioxidant properties of Citron fruit peel extract compared to the standard ascorbic acid. The Chloroform extract of citron fruit peel exhibited significant cytotoxic properties against FaDu (human hypopharyngeal tumour) cell line. The present study indicates the potential of the chloroform extract of C. medica fruit peel to be employed as an anthelmintic, antibacterial, antioxidant and anticancer agent. Hence, it emphasises the prominence that can be given to the dietary consumption of citrus fruit peel in various forms, such as dried peel, powder etc. The Author(s). -
Bioprospecting of Fungal Endophytes in Hulimavu Lake for Their Repertoire of Bioactive Compounds
Fungal endophytes hold a prominent position in the research world, in part due to the rich repertoire of bioactive compounds useful for industrial and environmental applications. The present study aims at bioprospecting few endophytic fungi isolated from Hulimavu lake flora (Bengaluru) for characterization of biological applications of their bioactive compounds. Among the lake plants screened, Alternanthera philoxeroides, Ricinus communis and Persicaria glabra were taken forward for isolation of fungal endophytes. Subsequent biochemical analyses were performed to quantify few fungal enzymes and bioactive compounds, followed by antimicrobial and cytotoxic assays. In conclusion, this pilot study aims to probe the plethora of bioactive compounds present in fungal endophytes that possess wide ranging biological properties. Due to the species richness and diversity of fungal endophytes across different host plants and habitats, bioprospecting fungal endophytes remains a very extensive yet promising topic for research, representing broad ranging environmental and industrial applications. The Electrochemical Society -
Differential Laccase Production among Diverse Fungal Endophytes in Aquatic Plants of Hulimavu Lake in Bangalore, India
The ability of plants to acclimatise and thrive in stressed environments can be attributed, in part, to the reserve of endophytic fungi that they harbour, that help enhance physiological and immunological defence and tolerance to various biotic and abiotic stressors. The present work has focussed on screening laccase producing endophytic fungi residing in different aquatic plants isolated from Hulimavu Lake, Bengaluru. This lake is well known for its water pollution contributed by anthropogenic factors. Survival of plants in this lake can hence be associated with their rich repertoire of endophytic fungi that enhance host plant defence towards stressors. Upon isolation and culturing of endophytic fungi, qualitative laccase detection using laccase specific growth media and quantitative laccase estimation using ABTS (2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) substrate were performed. Differential production rates were observed for the laccase enzyme by different endophytic fungi; production rates also varied between fungi isolated from different parts like node, stem, root and leaf of the same plant species too. Phylogenetic analysis of fungal isolates with highest laccase production was performed and the species was found to be Cladosporium tenuissimum. Even the crude extract of this strain displayed laccase production of 42.16U/L, as revealed by ABTS assay. Hence this strain is a promising candidate for optimization studies for utilisation in the domain of bioremediation and industrial applications. The Author(s) 2023. -
Personalized Explainable Transformer Models for Student Performance Prediction
The research presents a unified framework for forecasting student academic achievement using a transformer-based architecture supported by Explainable Artificial Intelligence techniques. The research is motivated by the need to combine predictive accuracy of transformer models with interpretability in student performance prediction. The framework applies an adapted Feature Tokenizer Transformer to the UCI Student Performance dataset and integrates SHAP and LIME methods to generate instance level, human readable explanations for each prediction. These explanations can help educators design targeted interventions. A Random Forest regressor is included as a baseline for comparison. The experiment results showed that the Random Forest performed slightly better than the Feature Tokenizer Transformer, which could be due to the small size of the dataset and certain features having a strong impact on the results. Nevertheless, the results show that modern deep learning models combined with personalized explainability offer a practical foundation for scalable solutions in more complex educational datasets, which helps connect high performing prediction models with actionable insights, contributing to the development of interpretable, data driven student support systems. 2025 IEEE. -
Securing her digital footprint: AI for women's safety
This chapter emphasizes the importance of artificial intelligence (AI) tools, analysis about the existing AI tools, and recommendations for future AI tools for women's safety. AI is experiencing significant growth and influence in the current era. Several key trends and developments highlight the role of AI in various domains: AI is being used for medical diagnosis, drug discovery, and patient care. Machine learning models are helping doctors analyse medical images, predict disease outcomes, and personalize treatment plans. Self-driving cars and drones are utilizing AI algorithms for navigation, obstacle detection, and decision-making. These technologies are advancing transportation and logistics. Natural language processing models like GPT-3 are transforming language-related tasks, from chatbots and virtual assistants to content generation, translation, and sentiment analysis. This chapter highlights the AI tools that exist for women's safety in the digital world and future apps needs for the same. 2024, IGI Global. All rights reserved. -
Deep Learning Algorithms Comparison forMultiple Biological Sequences Alignment
In this paper, deep learning algorithms are compared for aligning multiple biological molecular sequences such as DNA, RNA, and protein. Efficient algorithms are necessary for sequence alignment to identify significant insights, but there is a trade-off between time and accuracy. This study compares deep learning algorithms for multiple sequence alignment with better accuracy, using a new similarity measure to choose the best resemblance sequences in a set. Using a benchmark dataset, the algorithms compared include CNN, VAE, MLPNN, DBNs, Deep Boltzmann Machine, and GAN. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Equitable and inclusive online learning: A framework for supporting students with disabilities
Online learning has become a widely adopted mode of education, particularly during the COVID-19 pandemic. In general, individuals with disabilities face challenges when using non-technology components for studying. This chapter proposes a framework for equitable and inclusive online learning practices that support students with disabilities. The framework is based on a review of current research and best practices for online learning and disability accommodations. The framework emphasizes a collaborative, student-centered approach to online learning that acknowledges the unique needs and experiences of students with disabilities. Depending on the disabilities, the framework is divided into two phases namely: Prevalent Learning, and Discrete Learning. The former comprised components: Accessibility, Accommodation, and Engagement, and later has components like Methodology, Evaluation. The framework proposed provides a roadmap for addressing the challenges faced by students with disabilities in online learning environments. 2023 by IGI Global. All rights reserved. -
Community based open source geographical classical data analysis
The traditional Geographical Information Systems (GIS) have to be migrated to the internet eventually much like every other software today. The article has explored ways of utilizing the Open Geospatial Consortium (OGC) standards to come up with ways of achieving a workflow for the development of a service-based implementation of a customized Web Processing Service (WPS). The proposed concept has explored multiple workflows using various combinations of the publishing and development options and the simplest and the least resource intensive one has been identified as the outcome of this project. The workflow identified was then split into two section to make it even more simplify and adaptable, aiding development from the WPS that has to publish. The development process used for the final workflow is done without the use of a resource intensive IDE keeping in mind the major aim of the proposed model is to reduce the dependency on resource intensive software and services. The proposed model is built solely on open source platforms which are in tandem with second stipulation of proposed model is promoting community-based development. The proposed system provides the better execution time and retrieval time. The execution time is compared with similar system, open source Geographical system provide less execution time. The retrieval time is also reduced this indicated Quality of Service is increased. BEIESP. -
Cloud Enabled Smart Firefighting Drone Using Internet of Things
Internet of Things is fasted booming sector. This technology is evolved in various fields. The frequent updates in concerning the progress of Skyscraper fire or high-rise fire it is essential for us to ensure effective and safe firefighting. Since high-rise fire is typically inaccessible by ground vehicles due to some constraints or parameters. Due to less advancement in technology most skyscrapers are not furnished with proper fire monitoring and prevention system. To solve this issue this article is propose Unmanned Air Vehicles (UAVs) are making an appearance and making promises to prevent such kind of incidents. In this system, UAV can be launched from the Fire Control Unit (FCU). The proposed methodology is implemented with the help of Internet of Things (IoT). Sensors which are installed at the skyscraper detects the presence of fire and immediately send stress signals to the command and control unit from where further possible action can be taken. The pilot at the fire control unit continuously monitors the flight path and receives the video and fire scan information from the UAV. Upon detection of a stress signal or fire signal the Skyscraper position is determined with the help of Global Positioning System (GPS) and permission is requested from the applicable security agency to launch the extinguisher vehicle. The permission is granted, the coordinates of the location are filled in the system and the nearest station sends the UAV to the location. The fire suppressant are deployed it comes back to the nearest landing location and re-loaded with another fire suppressant to be carried to the fire location. The proposed methodology should improve the Quality of Service. 2019 IEEE. -
Threats and security issues in smart city devices
The main objective of this chapter is to discuss various security and privacy issues in smart cities. The development of smart cities involves both the private and public sectors. The theoretical background is also discussed in future growth of smart city devices. Thus, the literature survey part discusses different smart devices and their working principle is elaborated. Cyber security and internet security play a major role in smart cities. The primary solution of smart city security issues is to find some encryption methods. The symmetric and asymmetric encryption algorithm is analyzed and given some comparative statement. The final section discusses some possible ways to solve smart city security issues. This chapter showcases the security issues and solutions for smart city devices. 2022, IGI Global. -
Business transaction privacy and security issues in near field communication
The main objective of this chapter is to discuss various security threats and solution in business transactions. The basic working principle and theoretical background of near field communication (NFC) technology is discussed. A component of NFC communication section is to be discussed on various NFC operation modes and RFID tags. NFC technology is used in various fields such as electronic toll collection and e-payment collection for shopping. This device-to-device payment system is facing major security issues. This NFC communication data is transferred from one terminal to another terminal by using short-range radio frequency. Data hackers try to access this radio frequency and attack the business transaction. This hybrid encryption algorithm is used to solve business transaction data security issues. This chapter deals with both key encryption and data encryption processes. 2021, IGI Global. -
Parallel queue scheduling in Dynamic Cloud environment using Backfilling algorithm
Cloud Computing reshapes the entire computing paradigm. In general, cloud computing means outsourcing available services and data storage in centralized scenario. In cloud computing task allocation is a major problem because multiple numbers of tasks are allocated to multiple numbers of processors for simultaneous processing. From the given list, tasks are queued according to the ascending order based on their duration. This paper is designed to solve the Task Scheduling problem, by using our proposed effective new approach of Backfilling algorithm. Depending upon the task duration, tasks are split into multiple threads for processing. Multiple thread tasks are processed in the basic concept of "gang scheduling" technique. Here we implement new backfilling algorithm concept to minimize the idle processing time of the processors. The existing Simple Backfilling Algorithm (SBA) is used to minimize the ideal time processing. Whereas comparatively Dynamic Cloud Scheduling using Backfilling Algorithm (DCBA) is designed to reduce the ideal time processing than SBA to carry out the process of both LQueue and SQueue simultaneously. At the outset, DCBA reduces the average waiting time. As mentioned the algorithm which is specified in the previous line that contains three level which represent the working speed of the algorithm. The first and second level of DCBA algorithm is comparatively similar to the performance of SBA algorithm. The maximum better performance was given in a queue size (q=1.5) by DCBA algorithm as compare to SBA algorithm. The existing type (Gang Scheduling) consist of two approaches namely Adaptive First Come First Serve (AFCFS) and Largest Job First Served (LJFS) that focus on non-parallel jobs with deadline. When compare to existing gang scheduling algorithm and SBA algorithm the average waiting time of DCBA has slight improvement in the loader level of the key. As the separation of the queue like LQ and SQ the waiting time and average waiting time is reduced comparatively. 2018, Intelligent Network and Systems Society. -
Securing cloud data against cyber-attacks using hybrid aes with MHT algorithm
Cloud computing is dealing with large amount of data during data communication. This data processing is named as big data. The big data is growth of the demand in accessing the storage, computation and communication. This big data has the major defects. A raising issue in emerging big data is cost minimization. The architecture of big data ranges over multiple machines and cluster which have sub system. The major challenge of this big data is preprocessing and analysing the data patterns. This research article is dealing with different data pre-processing and secure data storage. There are many research challenges during this data process. The possible gap and drawbacks in the technology are identified through this survey and the efficient big data service is provided through MHT and AES algorithm. The main aim of this proposed method is to provide better data security during larger data process. The proposed hybrid MHT with AES algorithm is to minimize the encryption and decryption time apart from that it reduces the attacker ratio. All these parameters automatically increase the Quality of Service. Copyright Research Institute for Intelligent Computer Systems, 2020. All rights reserved. -
Cloud Dynamic Scheduling for Multimedia Data Encryption Using Tabu Search Algorithm
The cloud computing is interlinked with recent and out-dated technology. The cloud data storage industry is earning billion and millions of money through this technology. The cloud remote server storage is on-demand technology. The cloud users are expecting higher quality in minimal cost. The quality of service is playing a vital role in any latest technology. The cloud user always depends on thirty party service providers. This service provider is facing higher competition. The customer is choosing a service based on two parameters one is security and another one is cost. The reason behind this is all our personal data is stored on some third party server. The customer is expecting higher security level. The service provider is choosing many techniques for data security, best one is encryption mechanism. This encryption method is having many algorithms. Then again one problem is raised, that is which algorithm is best for encryption. The prediction of algorithm is one of major task. Each and every algorithm is having unique advantage. The algorithm performance is varying depends on file type. The proposed method of this article is to solve this encryption algorithm selection problem by using tabu search concept. The proposed method is to ensure best encryption method to reducing the average encode and decode time in multimedia data. The local search scheduling concept is to schedule the encryption algorithm and store that data in local memory table. The quality of service is improved by using proposed scheduling technique. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Viewers response towards brand placements credibility in reality shows: does brand image matter?
Purpose This study aims to investigate the impact of brand placements in reality shows on viewer perceptions. Specifically, it aims to explore whether the brand image of a reality show influences the credibility of these placements and how viewers respond to such integrations. By examining this relationship, the paper contributes valuable insights for marketers, advertisers and content creators seeking to optimise brand placements within reality television. Design/methodology/approach A conceptual model based on source credibility theory was developed. The hypothesised relationships were tested using the partial least squares-structural equation modelling method. Data was separately collected through a stimulus-based questionnaire. For the final data analysis, 393 usable questionnaires were used. Findings Through rigorous analysis, the authors unearthed several key findings. Firstly, brands with a positive image significantly enhance the credibility of their placements. Viewers are more receptive to products associated with reputable and relatable brands. Secondly, the seamless integration of brands within the shows narrative fosters authenticity. Viewers perceive placements as less intrusive and more credible when placements align with the programs context. Thirdly, not all viewers respond uniformly. Demographics, lifestyle and viewing habits influence how brand placements resonate. Tailoring strategies to specific audience segments is essential. Originality/value Prior research on brand placements predominantly concentrated on quantitatively investigating films and video games. Nevertheless, these studies have not included viewers consuming behaviours in reality shows. Previous empirical studies in reality shows have not developed a conceptual model incorporating the identified moderator (reality show brand image) to explain the impact of brand placements in reality shows. Finally, this study unravelled the complexities of viewer engagement and its potential impact on consumer behaviour by examining the moderating role of pre-existing brand image on audience receptivity. 2025 Emerald Publishing Limited