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Molecularly imprinted PEDOT on carbon fiber paper electrode for the electrochemical determination of 2,4-dichlorophenol
A highly selective electrochemical sensor has been developed for the determination of the pesticide molecule, 2,4-dichlorophenol (2,4-DCP) using molecularly imprinted conducting polymer. 2,4-dichlorophenol imprinted polymer films were prepared by electropolymerising 3,4-ethylenedioxythiophene (EDOT) on surface of carbon fiber paper electrode (CFP) in presence of 2,4-dichlorophenol. Electrochemical over-oxidation was carried out for the controlled release of 2,4-DCP templates and to generate definite imprinting sites. Surface morphology of the imprinted electrode was analysed by Scanning Electron Microscopy-Energy Dispersive X-ray Spectrometry, Fourier Transform Infrared and Raman spectroscopy. In optimized conditions, the voltammetric sensor gave a linear response in the range of 0.21 nM 300 nM. The significantly low detection limit (0.07 nM) demonstrates the ultra-low sensitivity of the method. The imprinted sensor displayed higher affinity and selectivity towards the target 2,4-DCP over similar structural analogical interference than the non-imprinted sensor. MIP sensor was efficaciously employed for the selective determination of 2,4-DCP in real samples of water. 2020 Elsevier B.V. -
Models for load forecasting and demand response
Increasing pressure on the utilities to accommodate energy efficiency, load management and progress in advanced technology has led to transformations of existing grids into smarter grids. With the development of Smart Grid Technology and the integration of smart meters it is possible to control the equipment installed at the consumer site. Creating awareness among the end- users to participate in load management programs instead of capacity addition is the best solution for maintaining the stability in the grid. Utilities can also encourage consumer participation in load control activities. They can ensure that power is given to a consumer during his priority time. For this, loads have to be categorized, prioritized and then considered for load shedding so that revenue loss and social impacts of load shedding are minimized. It would be beneficial if a consumer's load is not completely shed during load shedding. Amount of power that is shed from a consumer can be limited and consumers can be allowed to adjust their loads based on the availability of power and get incentives from the utilities for their change in load pattern. Consumers are also benefited with the reduced energy charges on the consumed energy during these periods. Review of the recent research work shows that demand response and load forecasting play an important role to relieve the power system from economic and environmental constraints. Various approaches have been used in the past for developing different demand response and forecasting methodologies including neural networks, fuzzy logic and statistical techniques. These methodologies fluctuate in complication, suppleness, and information necessity. In addition, statistical methods such as time series, regression, and state space methods have large numerical deviation in the predicted load series. In general, for accurate modeling of nonlinear and undecided type of load behavior, artificial intelligence-based techniques are employed. Also, these methods concentrate mainly on ordinary system conditions. However, proposing the possible Demand Response strategies to maintain power system security constraints in unpredicted turbulences pose a serious challenge. In the undertaken research, a novel load forecasting method using hybrid Genetic Algorithm Support Vector Regression model has been proposed. The forecast error is around 1-2%. The second part of the work focuses on formulation of demand response strategies based on time of the day and load prioritization. A Unique grading method has been proposed to prioritize the loads and load management during power deficiency by controlling the loads individually using different optimization techniques. The performance of three well recognized population based meta-heuristic algorithms such as Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization, to solve load management at the consumer level in the Smart Grid environment were examined in terms of their efficiency, effectiveness and consistency in obtaining the optimal solution. In the last part of the work the Demand Response model for residential load is proposed to minimize the energy cost of the electricity usage by shifting the loads from peak period to off-peak period with the help of intelligent techniques such as Artificial Bee Colony Algorithm. -
Models for load forecasting and demand response /
Increasing pressure on the utilities to accommodate energy efficiency, load management and progress in advanced technology has led to transformations of existing grids into smarter grids. With the development of Smart Grid Technology and the integration of smart meters it is possible to control the equipment installed at the consumer site. Creating awareness among the end users to participate in load management programs instead of capacity addition is the best solution for maintaining the stability in the grid. Utilities can also encourage consumer participation in load control activities. They can ensure that power is given to a consumer during his priority time. For this, loads have to be categorized, prioritized and then considered for load shedding so that revenue loss and social impacts of load shedding are minimized. It would be beneficial if a consumer's load is not completely shed during load shedding. Amount of power that is shed from a consumer can be limited and consumers can be allowed to adjust their loads based on the availability of power and get incentives from the utilities for their change in load pattern. Consumers are also benefited with the reduced energy charges on the consumed energy during these periods. -
Intelligent load shedding using ant colony algorithm in smart grid environment
For every country which is expecting a large growth in power demand in the near future or facing a power crisis, an effective load control and power distribution strategy is a necessity. Load shedding is done whenever power demand is more than power generation in order to sustain power system stability. The current load shedding strategies fails to shed exact amount of load as per the system requirement and does not prioritize loads which are being shed. Given the dimension of the problem, it would not be feasible computationally, to use regular optimization techniques to solve the problem. The problem is typically suited for application of meta-heuristic algorithms. This paper proposes a new scheme for optimizing load shedding using ant colony algorithm in a smart grid platform considering loads at utility level. The algorithm developed considers each electrical connection from Distribution Company as one lumped load and provides an effective methodology to control the load based on various constrains such as importance of load and time of load shedding. Springer India 2015. -
Demand response for residential loads using artificial bee colony algorithm to minimize energy cost
Power performance expectations are increasing, impacting designs and requiring advanced technology to improve system reliability. Demand Response (DR) is a highly flexible customer driven program in which customer voluntarily changes his energy usage patterns during the peak demand to maintain the system stability and reliability and thereby improves the performance of the gird. This paper proposes a novel algorithm for optimization of the DR schedule of the residential loads for various hours of the day using Artificial Bee Colony (ABC) algorithm. Here, the objective function is subjected to the constraints like cost constraints, time constraints and load demand. The results show that the proposed approach enhances potential in solving problems with good reliability compared with existing approaches. 2015 IEEE. -
Design and implementation of Adaptive PI control based dynamic voltage restorer for solar based grid integration
This paper introduces an innovative approach to address voltage fluctuations in solar-based grid integration by implementing an adaptive PI control-based Dynamic Voltage Restorer (DVR). This DVR is engineered to counteract voltage disruptions resulting from grid disturbances and the intermittent nature of solar energy generation. To achieve optimal performance in diverse operating conditions, the adaptive PI controller dynamically adjusts its parameters, adapting to changes in load and solar generation. The system is realized on a digital signal processor (DSP) and evaluated within a laboratory-scale solar-based grid integration setup. The findings reveal that the proposed system effectively mitigates voltage fluctuations, ensuring a stable integration of solar energy into the grid. The adaptive PI control-based DVR outperforms traditional PI control-based DVRs, particularly when dealing with variable solar energy generation. This approach holds significant potential for practical applications in solar-based grid integration systems. 2024 IEEE. -
Partial load shedding using ant colony algorithm in smart grid environment
Effective power distribution methodology is one of the basic necessities to meet the increasing power demand in any power system. Load shedding is done when power demand is more than power generation, to sustain the power system stability. Load shedding methods followed today shed a particular load completely, neglecting the critical consumers within the system. Controlling the loads at individual utility level in smart grid system enables us to put a maximum power limit on utility. Hence by partially shedding the load, demand can be reduced. The technique used here utilizes ant colony algorithm to choose a maximum power limit for each load dynamically based on the importance and priority of load. This method forces the consumer to manage his load internally based on criticality. It also effectively makes use of availability based tariff schemes. 2015 IEEE. -
Load shedding using GA and ACO in smart gird environment
Increasing pressure on the utilities to accommodate energy efficiency, load management and progress in advanced technology has led to transformations for existing grid into a smarter grid. Creating awareness among the end-users to participate in load management programs instead of capacity addition is the best solution for maintaining the stability in the grid. Load shedding is a strategy under load management in which load connected to the smart grid is individually controlled via two- way communication. In this paper, a Smart Load shedding approach is developed based on load prioritization. The required amount of load to be shed under lack of sufficient generation level is optimized by Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithms. The proposed approach is implemented using a real time feeder data from the substation, India. The results reflect the effectiveness of proposed algorithms taken into practical applications. -
Social networking- A comparative study on the new google + project and facebook /
This research project set out to study the new Google + Project, a new social network by Google. The dissertation looks at the social network as compared to already existing social networks especially Facebook, what the sight possesses that other social networks may not have and how successful the site will be in the years to come and if there is a need for a new social network after what is already there. The researcher went out to find out from users of the social networks, how popular Google plus and Facebook are and how frequently the sites are and what possible future the sites have. The researcher found out that Google plus isnt as popular as Facebook just yet but in years to come if they can learn from the mistakes that the already existing social network have made and develop on that to make the site more user friendly. -
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A critical analysis of the zimbabwean political leadership in the practice of justice
Political leadership is a fundamental philosophical issue influencing governance newlineof states and the success of every state is directly or indirectly linked to the newlineleadership ideology of its political leaders. This study investigated the nature and newlinecharacter of political leaders in Zimbabwe by assessing their political philosophy in the context of the major historical events since 1890 masked by three eras namely the pre-colonial era, (characterised by little political activities) followed by the colonial era (dominated by socio-political system that weakened the African culture and governance) and lastly, the post-independence era (characterised by failure to uphold the constitution by the ruling party leadership). newlineThe scope of this study is based on benchmarking the concepts of the Zimbabwean political philosophy with political ideas from renown philosophers such as Plato, Aristotle and Gandhi. More specifically, the Gandhian philosophy was selected and conceptually applied to the Zimbabwean political situation in an attempt to develop an ideal political philosophy because of its illustrious wisdom with regards to good governance principles. A hermeneutics and newlinephilosophical analytic models were used to interpret relevant literature and leadership responsibilities as provided for in the Zimbabwe Constitution. Study findings revealed challenges in the current political leadership that calls for developing a new Zimbabwean political leadership philosophy. These include but not limited to partisan politics, political violence, unbridled favouritism and nepotism, ethnicity in leadership, state capture of key institutions, negative use of power by the state, political violence, poor electoral systems flouted with impunity and rampant corruption. These political challenges militate against democratic principles of common good that fight human oppression, repression and suppression. -
BSLnO: Multi-agent based distributed intrusion detection system using Bat Sea Lion Optimization-based hybrid deep learning approach
Intrusion detection system (IDS) is a robust model that plays an essential role in dealing with intrusion detection, especially in detecting abnormal anomalies and unknown attacks. The major challenges faced by IDS are the computation time required for analysis, and the exchange of a huge amount of data from one division of the network to another. For the sake of tackling such limitations, this probe proposes a multi-agent enabled IDS for detecting intrusions using the Bat Sea Lion Optimization (BSLnO) algorithm. The proposed strategy consists of five phases, namely pre-processor agent, reducer agent, augmentation agent, classifier agent, and decision agent. Initially, input data is subjected to pre-processor agent, where pre-processing is carried out using data normalization and missing value imputation. Thereafter, the pre-processed result is fed up to the reducer agent, where dimension reduction is carried out using mutual information. The third step is data augmentation in which the dimensionality of data is enhanced. After that, the augmented result is subjected to classifier agent to classify intrusions or malicious activities present in the network based on hybrid deep learning strategies, namely deep maxout network and deep residual network. A developed BSLnO is implemented by incorporating Bat Algorithm (BA) and Sea Lion Optimization (SLnO) algorithm to train the hybrid classifier. The proposed scheme has acquired a higher precision of 0.936, recall of 0.904, and F-measure of 0.920. 2022 John Wiley & Sons Ltd. -
A stochastic propagation model to the energy dependent rapid temporal behaviour of Cygnus X-1 as observed by AstroSat in the hard state
We report the results from analysis of six observations of Cygnus X-1 by Large Area X-ray Proportional Counter (LAXPC) and Soft X-ray Telescope (SXT) onboard AstroSat, when the source was in the hard spectral state as revealed by the broad-band spectra. The spectra obtained from all the observations can be described by a single-temperature Comptonizing region with disc and reflection components. The event mode data from LAXPC provides unprecedented energy dependent fractional root mean square (rms) and time-lag at different frequencies which we fit with empirical functions.We invoke a fluctuation propagation model for a simple geometry of a truncated disc with a hot inner region. Unlike other propagation models, the hard X-ray emission (>4 keV) is assumed to be from the hot inner disc by a single-temperature thermal Comptonization process. The fluctuations first cause a variation in the temperature of the truncated disc and then the temperature of the inner disc after a frequency dependent time delay.We find that the model can explain the energy dependent rms and time-lag at different frequencies. 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
Machine Learning based Candidate Recommendation System using Bayesian Model
Online websites that recommend books, music, movies, and other media are becoming increasingly prevalent because of collaborative filtering. This online websites are using many algorithms to provide the better recommendation to attract the customer. Bayesian statistics, which is based on Bayes' theorem, is a technique for data analysis in which observable data are used to update the parameters of a statistical model. To discuss a strategy called item-based collaborative filtering, which bases predictions on the similarities between the said objects. This uses Machine Learning based Candidate Recommendation System which uses Bayesian Model database to assess the proposed method. The actual results show that for collaborative filtering which is based on correlation, the Bayesian techniques we have proposed outperform traditional algorithms. Also discuss a technique for improving prediction accuracy that combines the Simple Bayesian Classifier with user- and item-based collaborative filtering. The user-based recommendation is then applied to the matrix once the user-item rating matrix has been filled out with pseudo-scores produced by the item-based filter. This model is demonstrated that the combined approach outperforms the individual collaborative recommendation approach. The creation of UI based web application will help Students to manage achievement details. Job seekers and admin will be given a separately formatted version of the application where, students can upload and view their certificate, wherein admin can access student achievement details categorized by different parameters. This proposed model is developed under the service learning scheme to benefit both job seeker and recruiter. 2023 IEEE. -
A prediction technique for heart disease based on long short term memory recurrent neural network
In recent years, heart disease is one of the leading cause of death for both women and men. So, heart disease prediction is considered as a significant part in the clinical data analysis. Standard data mining techniques like Support Vector Machine (SVM), Naive Bayes and other machine learning techniques used in the earlier research for heart disease prediction. These methods are not sufficient for effective heart disease prediction due to insufficient test data. In this research, Bi-directional Long Short Term Memory with Conditional Random Field (BiLSTM-CRF) has been proposed to increase the efficiency of heart disease prediction. The input medical data were analyzed in a bidirectional manner for effective analysis, and CRF provided the linear relationship between the features. The BiLSTMCRF method has been tested on the Cleveland dataset to analyze the performance and compared with existing methods. The results showed that the proposed BiLSTM-CRF outperformed the existing methods in heart disease prediction. The average accuracy of the proposed BiLSTM-CRF is 90.04%, which is higher than the existing methods. 2020 by the authors. -
A Big Data Analysis Using Fuzzy Deep Convolution Network Based Model for Heart Disease Classification
Heart disease is a serious disease that causes sudden death among 80% of the people around the world. The traditional models performed predictive analytics using machine learning techniques to make a better decision. For better decision making in heart disease prediction, the big data analysis shows the great opportunities to predict the future health status from health parameters and provide best outcomes. However, the traditional decision making models had traffic data or contained noise and uncertainty was unpredictable as the data ambiguity emerged. In order to overcome such an issue, the big data is used to ensure the medical service which is mostly needed in a timely manner and for accurate diagnosis. The pre-processing of the medical data acquired from Cleveland heart disease UCI datasets has a vast number of attributes which helps to predict the heart disease. The data are contaminated with the noise and some of the data are missing, so the pre-processing using Min max Normalization is performed to remove contaminated noise acquired in the data which is taken from the UCI repository dataset. The proposed Fuzzy Deep Convolution Network (FDCN) permits the input features for fuzzification process that uses transformed features. The fuzzification process eliminates the redundant or irrelevant fuzzified features and overcomes the system complexity problems. The proposed FDCN obtains accuracy of 95.56 % and 92 % of F-score shows better results when compared with the existing KNN-DT, Naive Bayes, and Random Forest algorithms. 2021,International Journal of Intelligent Engineering and Systems. All Rights Reserved. -
A hybrid algorithm for face recognition using PCA, LDA and ANN
Face recognition is an evolving technique in the field of digital device security. The two procedures Principal Component Analysis and Linear Discriminant Analysis (LDA) are standard methodologies commonly used for feature extraction and dimension reduction techniques extensively used in the recognition of face system. This paper discourse, PCA trailed through a feed forward neural network (FFNN) called PCA-neural network and LDA trailed through feed forward neural network as LDA-neural network are considered for development of hybrid face recognition algorithm. In the current research work, a hybrid model of face recognition is presented with the integration of PCA, LDA, and FFNN. The proposed system experimental results indicate better performance compared to the state of the art literature methods. IAEME Publication. -
Experimenting with scalability of Beacon controller in software defined network
In traditional network, a developer cannot develop software programs to control the behavior of the network switches due to closed vendor specific configuration scripts. In order to bring out innovations and to make the switches programmable a new network architecture must be developed. This led to a new concept of Software Defined Networking(SDN). In Software defined networking architecture, the control plane is detached from the data plane of a switch. The controller is implemented using the control plane which takes the heavy lift of all the requests of the network. Few of the controllers used in SDN are Floodlight, Ryu, Beacon, Open Daylight etc. In this paper, authors are evaluating the performance of Beacon controller using scalability parameter on network emulation tool Mininet and IPERF. The experiments are performed on multiple scenarios of topology size range from 50 to 1000 nodes and further analyzing the controller performance. BEIESP. -
Ni-Co MOF Flowers/ZnO NRs Mediated Electrochemical Sensor for Rapid and Ultrasensitive Detection of Neotame in Food Samples
This study focuses on the bioreduction of waste-derived graphite rods into reduced graphene oxide(rGO), followed by the fabrication with Ni-Co metal-organic flowers and Zinc oxide nanorods(ZnO NRs) using Nafion, for sensitive detection of neotame. The Ni-Co metal-organic flowers and ZnO NRs were synthesized using solvothermal synthesis and Azadirachta indica leaf extract, respectively. Additionally, Nafion polymer enhances the stability and conductivity of the nanocomposite. The nanocomposite was characterized using UVvis, Fourier transform infrared spectorscopy, X-ray diffraction, Raman spectroscopy, Dynamic light scattering, X-ray photoelectron spectroscopy, Field-emission scanning electron microscopy, Energy-dispersive X-ray analsysis, Transmission electron microscopy, and Atomic force microscopy. The electrochemical studies were carried out using Electrochemical impedance spectroscopy and Cyclic voltammetry. The modified electrode (rGO/Nafion/Ni-Co MOF/ZnO NRs) demonstrated improved electrochemical activity (34.01 ?A) for neotame with an enhanced peak current at +0.73 V. The LOD and LOQ values were calculated and found to be 0.32 and 0.99 ?M with a recovery (%) ranging from 94.50 to 101.34%. The outcome of this study identifies the morphological and electrochemical factors as major contributors to the adsorption affinities and catalytical activities, with promising possibilities for the design of electrochemical sensing of artificial sweeteners. 2024 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Construction of a waste-derived graphite electrode integrated IL/Ni-MOF flowers/Co3O4 NDs for specific enrichment and signal amplification to detect aspartame
A novel and cost-efficient electrochemical sensor was designed by immobilizing IL/Ni-MOF/Co3O4 nanodiamonds on the graphite (GE) electrode, marking the first application for the detection of aspartame. The graphite electrode was extracted and recycled from discharged batteries to serve as a working electrode. The nanocomposite features unique Co3O4 nanodiamonds, generated using Coriandrum sativum seed extract, alongside Ni-metal organic framework (MOF), which were synthesized through a solvothermal method. The conductivity and stability of the electrochemical sensor were enhanced through the incorporation of the ionic liquid (IL) ([BMIM][MeSO4]. The phytochemical profile of Coriandrum sativum seed extract, analyzed by GC-MS, identified key compounds involved in the synthesis of Co3O4 nanodiamonds. A comprehensive characterization of the nanocomposite was performed using UV-Vis, FTIR, DLS, Zeta potential, XRD, XPS, FE-SEM, TEM, optical profilometry, and AFM to confirm the structural and elemental modifications. Electrochemical characterization of the bare and modified electrodes was conducted through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The GE/IL/Ni-MOF/Co3O4 nanodiamonds modified electrode displayed enhanced electroanalytical performance for aspartame detection, characterized by signal amplification at +7.0 V. Quantitative analysis by Differential Pulse Voltammetry (DPV) and Square Wave Voltammetry (SWV) revealed a linear detection range of 315 M for aspartame. A comparison of SWV and DPV revealed superior analytical performance for SWV, with limit of detection (LOD) and limit of quantification (LOQ) values of 1.02 M and 3.1 M (R2 = 0.993) compared to 1.81 M and 5.5 M (R2 = 0.986) for DPV. This study reveals the excellent adsorption capabilities of Ni-MOF and Co3O4 nanodiamonds (Co3O4 NDs), attributed to their high porosity and large surface area, paving the way for the development of affordable sensing devices for artificial sweeteners. 2024 Elsevier B.V.