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An in-Depth Analysis on the Cumulative Effect of Co and Sintering Temperatures on the Formaldehyde Sensing Attributes of NiO
In-depth studies are availing to explore and utilize the sensing attributes of p-type NiO nanostructures. However, the surface functionalization of NiO using Co for gas sensing along with varying temperature profile is a novel attempt till date. The research succeeded in synthesizing pure and substituted NiO via co-precipitation route and assessed the sensing capability of the samples by testing with 10 different target gases. The Co doped NiO sintered at 500C exhibited promising sensing performance within a concentration range of 1100ppm, notably achieving a high response of 7817 for 100ppm HCHO at room temperature. The proposed sensor demonstrated rapid response and recovery times (9s and 8s), and it successfully passed stability tests conducted over a 30-day period and repeatability tests consisting of eight cycles. The work paved a way to the implication of the prepared sensor as a breath analyzer to detect lung cancer due to its appreciable formaldehyde sensing characteristics. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Modelling of Cointegration with Students T-errors
Two or more non-stationary time series are said to be co-integrated if a certain linear combination of them be-comes stationary. Identification of co-integrating relationships among the relevant time series helps the researchers to develop efficient forecasting methods. The classical approach of analyzing such series is to express the co-integrating time series in the form of error correction models with Gaussian errors. However, the modeling and analysis of cointegration in the presence of non-normal errors needs to be developed as most of the real time series in the field of finance and economics deviates from the assumption of normality. This paper focuses on modeling of a bivariate cointegration with a students-t distributed error. The co-integrating vector obtained from the error correction equation is estimated using the method of maximum likelihood. A unit root test of first order non stationary process with students t-errors is also defined. The resulting estimators are used to construct test procedures for testing the unit root and cointegration associated with two time series. The likelihood equations are all solved using numerical approaches because the estimating equations do not have an explicit solution. A simulation study is carried out to illustrate the finite sample properties of the model. The simulation experiments show that the estimates perform reasonably well. The applicability of the model is illustrated by analyzing the data on time series of Bombay stock exchange indices and crude oil prices and found that the proposed model is a good fit for the data sets. 2022 by authors, all rights reserved. -
Impact of Variable Distributed Generation on Distribution System Voltage Stability
With advances in renewable energy (RE)technologies and the promotion of restructuring, distributed energy (DG)sources play a vital role in today's power sector. From the technical and economic point of view, DG sources provide a no of benefits such as lesser system losses, better system voltage profile and lower line congestion. The aim of this work is to determine the voltage stability of a distribution system at different levels of DG compensation determined as a percentage of the total load on the system. The objective function is formulated to minimize the real power loss. At first, the locations are chosen based on strategy using Loss Sensitivity Factors (LSF)and the optimal sizing of multiple units of DG sources is optimized using Particle Swarm Optimization (PSO)algorithm. The simulations are performed on standard IEEE 33-bus and 69-bus test systems and the results validate the importance of placing appropriately sized DG sources at suitable locations to achieve improved voltage stability and reduced distribution losses. 2019 IEEE. -
Teaching Learning-Based Optimization with Learning EnthusiasmMechanism for Optimal Control of PV Inverters in Utility Grids for Techno-Economic Goals
This study presents the optimal placement and operation of distributed generation (DG) sources in a distribution system embedded with utility-owned DG sources. Cost minimization and technical improvement of the network are the key objectives of the distribution company (DisCo). With the increasing popularity for renewable energy sources, DisCos are installing their own DGs to fulfill their electricity demand partially. When DisCos are the DG owners, the technical and economic considerations overlap. A novel method is proposed in this paper based on the recent variant of the teaching learning-based optimization (TLBO) algorithm and learning enthusiasm-based TLBO (LebTLBO) to optimize locations, sizes, and operational power factors of DGs in a distribution system with DisCo-owned DGs. A multi-objective function to improve voltage stability, reduce distribution losses, and reduce energy costs has been considered for solving the problem. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Optimal DG Planning and Operation for Enhancing Cost Effectiveness of Reactive Power Purchase
The demand for reactive power support from distributed generation (DG) sources has become increasingly necessary due to the growing penetration of DG in the distribution network. Photovoltaic (PV) systems, fuel cells, micro-turbines, and other inverter-based devices can generate reactive power. While maximizing profits by selling as much electricity as possible to the distribution companies (DisCos) is the main motive for the DG owners, technical parameters like voltage stability, voltage profile and distribution losses are of primary concern to the (DisCos). Local voltage regulation can reduce system losses, improve voltage stability and thereby improve efficiency and reliability of the system. Participating in reactive power compensation reduces the revenue generating active power from DG, thereby reducing DG owners profits. Payment for reactive power is therefore being looked at as a possibility in recent times. Optimal power factor (pf) of operation of DG becomes significant in this scenario. The study in this paper is presented in two parts. The first part proposes a novel method for determining optimal sizes and locations of distributed generation in a radial distribution network. The method proposed is based on the recent optimization algorithm, TeachingLearning-Based Optimization with Learning Enthusiasm Mechanism (LebTLBO). The effectiveness of the method has been compared with existing methods in the literature. The second part deals with the determination of optimal pf of operation of DG sources to minimize reactive power cost, reduce distribution losses and improve voltage stability. The approachs effectiveness has been tested with IEEE 33 and 69 bus radial distribution systems. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Optimal Load Control for Economic Energy Equilibrium in Smart Grid Using Adaptive Inertia Weight Teaching-Learning-Based Optimization
Due to numerous operational restrictions and economic purposes, optimal load management for energy balance in the smart grid (SG) is one of the compensating responsibilities. This research provides a novel multiobjective optimization technique for attaining energy balance in SG, with the goal of avoiding fines due to excessive upstream network power extraction beyond contractual demand. Due to a lack of capacity to create the whole optimization towards the global optimum after each run, optimal load control (OLC) is a prevalent challenge. Adaptive-TLBO, the most recent variation of Teaching Learning Based Optimization (TLBO), comprises both alterations during the exploitation and exploration phases (ATLBO). Because the ATLBO is used on a modified IEEE 33-bus system, the results obtained in this mode are extraordinary. The energy balance has improved in addition to the enhancement of the voltage profile and the reduction of distribution losses. As evidenced by comparisons with PSO, basic TLBO, backtracking search algorithm (BSA), and cuckoo search algorithms, the suggested ATLBO algorithm has precedence over any other proposed algorithm (CSA) 2022, International Journal of Intelligent Engineering and Systems.All Rights Reserved. -
An adaptive inertia weight teachinglearning-based optimization for optimal energy balance in microgrid considering islanded conditions
The energy balance in islanded microgrids is a complex task due to various operational constraints. This paper proposes a new approach to multi-objective optimization for achieving energy balance in aMicrogrid(MG) in both islanded and normal modes. Optimal load control (OLC)is achallenge, due to a lack of capacity to generate the global optimum after each run. The latest variant of Teaching Learning Based Optimization (TLBO), known as Adaptive-TLBO, includes both modifications during exploitation and exploration stages (ATLBO). The results achievedwith the proposed method are exceptional on a modified IEEE 33-bus system. In addition to the improvement of the voltage profile and the decrease of the distribution losses, the energy balance improves with the method. The proposed ATLBO algorithm overrides any proposed other algorithm, as shown by comparison with PSO, base TLBO, Backtrackingsearch algorithm (BSA) and cuckoo search algorithms, etc. (CSA). The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. -
Informal Waste Recycling in Dharavi A Marxist Feminist Reading of The Women of Wasteland
[No abstract available] -
Two dimensional fuzzy context-free languages and tiling patterns
Fuzzy context-free languages are powerful compared to fuzzy regular languages as they are generated by fuzzy context-free grammars and fuzzy pushdown automata, which follow an enhanced computational mechanism. A two dimensional language (picture language) is a collection of two dimensional words, which are a rectangular array of symbols made up of finite alphabets. Two dimensional automata can recognize two dimensional languages that could not be recognized by one dimensional automata. In this paper, we introduce two dimensional fuzzy context-free languages generated by the two dimensional fuzzy context-free grammars and accepted by the two dimensional fuzzy pushdown automata in order to deal with the vagueness that arises in two dimensional context-free languages. We can construct a two dimensional fuzzy context free grammar from the given two dimensional fuzzy pushdown automata and vice versa. In addition, we prove that two dimensional fuzzy context-free languages are closed under union, column concatenation, column star, homomorphism, inverse homomorphism, reflection about right-most vertical, reflection about base, conjugation and half-turn and also show that two dimensional fuzzy context-free languages are not closed under matrix homomorphism, quarter-turn and transpose. Further, we have given the applications and the uses of closure properties in the formation of tiling patterns. 2024 Elsevier B.V. -
On Two-Dimensional Approximate Pattern Matching Using Fuzzy Automata
Pattern matching has been extensively studied in the last few decades, owing to its great contribution in various fields such as search engines, computational biology, etc. Several real-life situations require patterns that allow ambiguity in specified positions. In this paper, one-dimensional and two-dimensional approximate pattern matching models have been constructed using fuzzy automata. The similarity function used in fuzzy automata enables the occurrence of all exact and similar one-dimensional and two-dimensional patterns. This kind of searching approximate patterns is not possible with regular search models. The time complexity of the proposed algorithm has also been analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
PICTURE PROCESSING ON ISOMETRIC FUZZY REGULAR ARRAY LANGUAGES
Isometric array grammar is one of the simplest model to generate picture languages, since both sides of its production rule have the same shape. In this paper, we have introduced isometric fuzzy regular array grammars to generate isometric fuzzy regular array languages and discussed its closure properties. Also, the relation between isometric fuzzy regular array grammar and boustrophedon fuzzy finite automata has been discussed. Moreover, we study the relation between two dimensional fuzzy regular grammars with returning fuzzy finite automata and boustrophedon fuzzy finite automata. Further, the hierarchy results of these three classes of languages have been discussed. 2024 KSCAM. -
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Quantitative X-ray and Spectroscopic Analysis of Nanocrystalline and Amorphous Carbon Materials
Carbon and its various allotropic forms is a blooming and extensively investigated field for the past few decades. The revolution which started with the discovery of fullerenes in 1985 continues with the newly discovered wonder material graphene and has never failed to amass the interest of scientific community. After all these years it still stays as a hot topic of research. This is primarily due to their unique physical and chemical properties which makes them suitable for a whole host of applications ranging from thin film technology to nano-medicine. But, the production cost of these novel materials is an issue which shadows its glory and hence it is essential to find out efficient and cost effective sources and production methods for these materials. Graphene oxide has attracted much interest because of its low cost, easy access and unique ability to get converted into graphene. Graphene oxide is basically, a graphene sheet which consists of either carboxyl or hydroxyl groups. Foreseeing the upcoming era of carbon nanomaterials on account of their revolutionary applications and the ever increasing demand for economical and viable sources, we have identified and explored the structural parameters of an efficient and cost effective precursor of the same. In the present investigation, wood charcoal and coconut shell charcoal, which is a superior source of activated carbon, is produced by a slow thermal decomposition method in a limited supply of oxygen. It is an impure form of carbon- is a black residue composed mainly of carbon, ash and char. Wood charcoal is transformed into Graphite oxide (GO) by a modified Hummers method. Spectroscopic analysis of the samples is carried out by various techniques such as X-ray diffraction (XRD), Raman Spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), X-ray Photoelectron Spectroscopy (XPS), UV-Vis spectroscopy and Scanning Electron Microscopy (SEM). The various structural parameters are calculated from XRD and Raman data. -
Integrating AI Tools into HRM to Promote Green HRM Practices
The image of Human Resource Management (HRM) is undergoing a drastic transformation. The conventional methods are evolving due to the emergence of technology, especially with the integration of Artificial Intelligence (AI) and data analytics into the HR processes. With the rapidly changing concept of the overall growth of an organization, AI is becoming a vital stimulant for sustainable growth. AI-powered tools promote data-driven decision-making for talent acquisition, performance management, workforce training and development, optimization of energy consumption and waste reduction. Green HRM aligns these efforts by integrating sustainability considerations into talent management strategies, nurturing employees eco-engagement, and promoting environmentally responsible practices within the workforce. This research paper aims to explore the synergies between AI tools and Green HRM practices, investigating how the integration of AI technologies into HR processes can contribute to the promotion of environmental sustainability. By examining real-world case studies, this study aims to investigate the potential of AI-powered solutions in shaping the future of HRM through the lens of sustainability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
IoT-Enabled Analysis of COVID Data: Unveiling Insights from Temperature, Pulse Rate, and Oxygen Measurements
The COVID-19 pandemic has forced unparalleled transformation on healthcare systems around the world, demanding new and improved approaches for effective monitoring and diagnosis. In this context, we present a study titled IoT-Enabled Analysis of COVID Data: Unveiling Insights from Temperature, Pulse Rate, and Oxygen Measurements. The global impact of COVID-19, with millions of confirmed cases and fatalities, underscores the urgency of finding efficient monitoring solutions. To address this crisis, IoT-Enabled Health Monitoring Systems have emerged as a promising tool for remote patient monitoring and infection risk reduction. These systems leverage sensors to collect real-time data on the temperature, pulse rate, and oxygen saturation levels of the subject. The integration of a mobile application enables immediate access to this critical health information. In this study, we explore the use of IoT systems, which have demonstrated accuracy comparable to other devices on the market. By leveraging these technologies, we aim to provide healthcare professionals with valuable insights into patients health status, aiding in early detection, monitoring, and timely intervention. Our research contributes to the efforts to battle the COVID-19 pandemic by highlighting the potential of IoT-enabled monitoring systems in enhancing healthcare delivery, reducing infection risks, and ultimately saving lives. 2024 Scrivener Publishing LLC. -
Looking beyond leadership mantras
The Key is not to Reject the Leadership theories, But to Supplement them with the Intellectual Depth that the Humanities and Social Sciences Can Provide, Writes P Jhon Kennedy -
The Curious Political Phenomenon of Shashi Tharoor's Praise For Modi and the Congresss Dilemma
If Tharoor is indeed preparing for a new political chapter, he should turn the page clearly. -
Indias Struggle Continues With Gender Equality: A Long Way To Go
While some countries inch forward, others remain stagnant or regress. India, unfortunately, falls into the latter group. Ranked 131 out of 148 countries, Indias gender parity score is just 64.1 per cent, making it one of the lowest globally -
Women in STEM: Act beyond enrolment
While India boasts of a rising proportion of female STEM graduates, their participation in tech sector jobs remains low. This and other Asian examples show focusing on enrolment is ineffective in boosting equality without parallel efforts to dismantle workplace hurdles -
Necessary toolkit
The 2025 UGC notification and the proposed caste survey, one offering legal legitimacy, the other empirical clarity, are both necessary