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Optical Resonator-Enhanced Random Lasing using Atomically Thin Aluminium-based Multicomponent Quasicrystals
Photon trapping inside a gain medium using a dispersed two-dimensional (2D) passive scatterer is an impetus to obtain incoherent random lasing (ic-RL) emission due to non-resonant feedback. An optical resonator (OR) can be used to influence such lasing thresholds. Non-noble nanomaterials-based quasicrystals (QCs) are an intriguing research prospect due to their potential surface plasmon resonance (SPR) property and ability to be exfoliated into 2D. In this work, an aluminium-based multicomponent alloy (Al70Co10Fe5Ni10Cu5) has been synthesized via the arc melting method. Thereafter, ultrasonication-based liquid phase exfoliation was used to obtain 2D quasicrystals (2D-QCs). The SPR-induced light scattering properties of synthesized 2D-QCs were exploited to obtain ic-RL from DCM dye gain medium under 532 nm, 10 ns, 10 Hz pulsed laser pumping. The plasmonic field enhancement property of 2D-QCs which enables the gain medium to absorb photons outside its peak absorption band has been demonstrated. The transition from ic-RL to OR-enhanced ic-RL and vice versa in the presence of resonator walls has been achieved by tweaking the device architecture. In this way, the ability of 2D-QCs to be potential passive scatterers and the controllability of lasing thresholds in the presence of an OR has been demonstrated. 2024 Elsevier Ltd -
Optical Spectroscopy of Classical Be Stars in Old Open Clusters
We performed the optical spectroscopy of 16 classical Be stars in 11 open clusters older than 100 Myr. Ours is the first spectroscopic study of classical Be stars in open clusters older than 100 Myr. We found that the H? emission strength of most of the stars is less than 40 in agreement with previous studies. Our analysis further suggests that one of the stars, [KW97] 35-12, might be a weak H? emitter in nature, showing H? equivalent width of ?0.5 Interestingly, we also found that the newly detected classical Be star LS III +47 37b might be a component of the possible visual binary system LS III +47 37, where the other companion is also a classical Be star. Hence, the present study indicates the possible detection of a binary Be system. Moreover, it is observed that all 16 stars exhibit a lesser number of emission lines compared to classical Be stars younger than 100 Myr. Furthermore, the spectral type distribution analysis of B-type and classical Be stars for the selected clusters points out that the existence of CBe stars can depend on the spectral type distribution of B-type stars present in these clusters. 2023. National Astronomical Observatories, CAS and IOP Publishing Ltd. -
Optical spectroscopy of Gaia detected protostars with DOT: Can we probe protostellar photospheres?
Optical spectroscopy offers the most direct view of the stellar properties and the accretion indicators. Standard accretion tracers, such as H ? , H ? and Ca II triplet lines, and most photospheric features fall in the optical wavelengths. However, these tracers are not readily observable from deeply embedded protostars because of the large line of sight extinction (Av? 50 100 mag) toward them. In some cases, however, it is possible to observe protostars at optical wavelengths if the outflow cavity is aligned along the line-of-sight that allows observations of the photosphere, or the envelope is very tenuous and thin, such that the extinction is low. In such cases, we not only detect these protostars at optical wavelengths, but also follow up spectroscopically. We have used the HOPS catalog (Furlan et al. in 2016) of protostars in Orion to search for optical counterparts for protostars in the Gaia DR3 survey. Out of the 330 protostars in the HOPS sample, an optical counterpart within 2 ? ? is detected for 62 of the protostars. For 17 out of 62 optically detected protostars, we obtained optical spectra (between 5500 and 8900 using nt Object Spectrograph and Camera (ADFOSC) on the 3.6-m Devasthal Optical Telescope (DOT) and Hanle Faint Object Spectrograph Camera (HFOSC) on 2-m Himalayan Chandra Telescope (HCT). We detect strong photospheric features, such as the TiO bands in the spectra (of 4 protostars), hinting that photospheres can form early in the star-formation process. We further determined the spectral types of protostars, which show photospheres similar to a late M-type. Mass accretion rates derived for the protostars are similar to those found for T-Tauri stars, in the range of 10 - 7 10 - 8M? yr - 1 . 2023, Indian Academy of Sciences. -
Optical spectroscopy of Galactic field classical Be stars
In this study, we analyse the emission lines of different species present in 118 Galactic field classical Be stars in the wavelength range of 3800-9000 We re-estimated the extinction parameter (AV) for our sample stars using the newly available data from Gaia DR2 and suggest that it is important to consider AV while measuring the Balmer decrement (i.e. D34 and D54) values in classical Be stars. Subsequently, we estimated the Balmer decrement values for 105 program stars and found that ?20 per cent of them show D34 ? 2.7, implying that their circumstellar disc are generally optically thick in nature. One program star, HD 60855 shows H? in absorption - indicative of disc-less phase. From our analysis, we found that in classical Be stars, H? emission equivalent width values are mostly lower than 40 which agrees with that present in literature. Moreover, we noticed that a threshold value of ?10of H? emission equivalent width is necessary for FeII emission to become visible. We also observed that emission line equivalent widths of H?, P14, FeII 5169, and OI 8446for our program stars tend to be more intense in earlier spectral types, peaking mostly near B1-B2. Furthermore, we explored various formation regions of Ca II emission lines around the circumstellar disc of classical Be stars. We suggest the possibility that Ca II triplet emission can originate either in the circumbinary disc or from the cooler outer regions of the disc, which might not be isothermal in nature. 2021 Oxford University Press. All rights reserved. -
Optimal Allocation of Renewable Sources with Battery and Capacitors in Radial Feeders for Reliable Power Supply Using Pathfinder Algorithm
Allocating renewable energy systems (RESs) in an electrical distribution system (EDS) is crucial to achieving various objectives. However, their intermittency presents several challenges. In this connection, an efficient meta-heuristic pathfinder algorithm (PFA) is employed to determine the optimal location and size of photovoltaic (PV) and wind turbine (WT) systems, along with energy storage systems (ESS) and capacitor banks (CB) for both grid and islanding modes of operations. An objective function was formulated for loss reduction, greenhouse gas (GHG) emissions, and voltage profile improvement. The simulation results for the IEEE 33-bus EDS system are shown for two cases: grid-connected and islanding. The computational effectiveness of the PFA was compared with that reported in the literature. The PFA results showed an outstanding ability to resolve difficult optimisation problems. In addition, the optimal size of the RES when the network operates in the grid-connected mode can significantly improve the performance. The real power losses and GHG emissions were reduced by 48.49 % and 67.75% with PV systems and the other, respectively, whereas WT systems they are reduced to 69.68 % and 67.85 %, respectively. However, a combination of ESS, CB, and PV/WT can render the EDN sustainable for the islanding mode of operations. The Author(s). -
Optimal allocation of solar photovoltaic distributed generation in electrical distribution networks using Archimedes optimization algorithm
This paper proposes to resolve optimal solar photovoltaic (SPV) system locations and sizes in electrical distribution networks using a novel Archimedes optimization algorithm (AOA) inspired by physical principles in order to minimize network dependence and greenhouse gas (GHG) emissions to the greatest extent possible. Loss sensitivity factors are used to predefine the search space for sites, and AOA is used to identify the optimal locations and sizes of SPV systems for reducing grid dependence and GHG emissions from conventional power plants. Experiments with composite agriculture loads on a practical Indian 22-bus agricultural feeder, a 28-bus rural feeder and an IEEE 85-bus feeder demonstrated the critical nature of optimally distributed SPV systems for minimizing grid reliance and reducing GHG emissions from conventional energy sources. Additionally, the voltage profile of the network has been enhanced, resulting in significant reductions in distribution losses. The results of AOA were compared to those of several other nature-inspired heuristic algorithms previously published in the literature, and it was observed that AOA outperformed them in terms of convergence and redundancy when solving complex, non-linear and multivariable optimization problems. The Author(s) 2022. -
Optimal arrangement of ration items into container using modified forest optimization algorithm
Planning a shrewd framework for loading the ration goods into the container is one of the significant objectives in the mission of smart city advancement in India. This optimal container loading system is designed for the arrangement of ration goods into the container using Modified Forest Optimization algorithm for safe and secure delivery. The effectiveness of the proposed approach has been demonstrated using BR datasets and compared with different optimization algorithms. From the experiment, it is observed that the proposed Modified Forest Optimization algorithm is implemented in java meets the objective of loading the ration items into the container in an optimal fashion. Further, it is observed that the order of arrangement predicted by the proposed algorithm is found to be optimal than other competitive optimization algorithms. 2020, Engg Journals Publications. All rights reserved. -
Optimal design of controller for automatic voltage regulator performance enhancement: a survey
For regulating the Synchronous Generator (SG) output voltage, the Automatic Voltage Regulator (AVR) system is a significant device. This work propounds a survey on Optimization Algorithms (OAs) utilized for tuning the controller parameters on the AVR system. A device wielded for adjusting the SGs Terminal Voltage (TV) is named AVR. A Controller is utilized for improving stability and getting a superior response by mitigating maximum Over Shoot (OS), reducing Rise Time (RT), reducing Settling Time (ST), and enhancing Steady State Error (SSE) since output voltage has a slower response and instability. The controllers utilized here are Proportional-Integral-Derivative (PID), Intelligent Controller (IC), along with Fraction Order PID (FOPID). Owing to the occurrence of time delays, nonlinear loads, variable operating points, and others, OAs are wielded for tuning the controller. (a) Particle Swarm Optimization (PSO), (b) Genetic Algorithm (GA), (c) Gray Wolf Optimizer (GWO), (d) Harmony Search Algorithm (HSA), (e) Artificial Bee Colony (ABC), (f) Teaching Learned Based Optimization (TLBO), et cetera are the various sorts of OA. For enhancing the TV response along with stability, various OAs were tried by researchers. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Optimal Feature Selection for the Classification of Hyperspectral Imagery Using Adaptive SpectralSpatial Clustering
Hyperspectral images captured through the hyperspectral sensors play an imperative part in remote sensing applications in the present context. Unlike traditional images sensed with few bands in the visible spectrum, the hyperspectral (HS) images are obtained with hundreds of spectral band ranges from infrared to ultraviolet regions. Because of its vast spatial and spectral data, it requires an extensive computational system for processing and its hidden features are needed to be unveiled in an effective manner specifically for the classification of HS imagery. This approach exploits the high spectral band correlation and rich spatial information of the HS images for the generation of feature vectors. To attain optimal feature space for the best probable classification, an adaptive approach is incorporated to adaptively choose spectralspatial features for feature selection to classify the pixels effectively. Furthermore, the HS image encompasses several bands including noisy bands. To categorize the images with great accuracy, it is suggested to eradicate the noisy bands whilst retaining the informative bands. In this research, an adaptive spectralspatial feature selection scheme is proposed for HS images where the extremely correlated representative bands are considered for analysis with uncorrelated and noisy spectral bands are judiciously discarded during its classification process. This hybrid approach not merely diminishes the computational time and also improves the general classification accuracy significantly. The empirical result displays that the proposed work surpasses the conventional approach of HS image classification systems. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
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. -
Optimal order quantity with endogenous discounted partial advance payment and trade-credit for inventory model with linear time varying demand
When buyers seek extended credit periods from relatively less secure suppliers, the supplier requires co-operation from the buyer for purchase of raw materials, etc., in terms of partial advance payment. For a single buyer single supplier supply chain, we develop an inventory model with a hybrid payment policy combining discounted advance payment to supplier with the supplier permitting interest free delay period to the buyer when buyer has the option to make advance payment. For the advance, supplier offers discount and may allow the buyer to set the discount rate and the advance proportion. For a linear demand function in such a scenario, we maximise buyer's net profit rate through optimal choice of payment policy and the buyer's replenishment. We consider two situations with discount: 1) exogenous; 2) endogenous for the buyer. Optimal solution is characterised theoretically. Numerical example reveals that by choosing advance proportion appropriately, the profit rate in the endogenous case can be higher than that in the exogenous case even with lower discount. 2021 Inderscience Enterprises Ltd.. All rights reserved. -
Optimal ordering and discounting policy for a segmented market with price and freshness dependent demand for mixed quality product
Owing to various factors, fresh produce purchased by the retailer is initially of mixed quality. A random proportion of the lot would generally have lost some freshness before being received in stock, while the remaining items would still be fresh. This calls for some discount initially for the former, and later, when the latter product is not so fresh. For demand declining with increase in selling price and decrease in freshness, this paper deals with optimal ordering and discounting policy when the lot received is of mixed quality and the market has two segments differentiated by the initial product quality sold simultaneously at widely different prices. Sufficient conditions for existence and uniqueness of optimal cycle length and the optimal discount are obtained. Sensitivity analysis reveals that increase in freshness time and proportion of initially fresh items in the lot result in increased profit rate. Copyright 2024 Inderscience Enterprises Ltd. -
Optimal portfolio construction with nifty stocks /
International Journal of Interdisciplinary and Multidisciplinary Studies, Vol.1, Issue 4, pp.474-480, ISSN No: 2348-0343. -
Optimal procurement and pricing policy for deteriorating items with price and time dependent seasonal demand and permissible delay in payment
In practice, items like food, nursery plants, medicines, etc. are seasonal and deteriorating in nature. For this type of products, permissible delay in payment is a common business policy, which is used to increase in the sell volume and to develop trust in buyer-seller relationship. In this paper, we developed an inventory model for time dependent deteriorating seasonal items with the permission of delay in payment. Shortages are permitted and partially back ordered. Our aim is to find optimal selling price and ordering quantity simultaneously. Concavity of profit function with respect to decision variables has been discussed analytically. A solution procedure followed by a numerical example and sensitivity analysis along with managerial insights are provided. Numerical analysis predicts that delay in payment profit policy is a better decision in order to maximise the profit or in order to get more profit. 2022 Inderscience Enterprises Ltd. -
Optimal procurement policy for growing items under permissible delay in payment
In the last decade, growing item industries have shown an increasing trend in production and it is expected that such industries will maintain this increasing pace in the future. Existing challenges of these industries, like mortality in the production phase and deterioration in the consumption phase, make procurement decisions more complex. In this article, we established an inventory model with mortality, deterioration, and price-dependent demand. To increase the sales volume and profit, a delay in payment policy is considered. A numerical example is presented to explain the solution procedure. The concavity of the profit function is discussed analytically for decision variables. It has been observed through sensitivity analysis that selling price is the most sensitive among decision variables and parameters. 2024 Inderscience Enterprises Ltd. -
Optimal Siting of Capacitors in Distribution Grids Considering Electric Vehicle Load Growth Using Improved Flower Pollination Algorithm
The optimal VAr compensation using capacitor banks (CBs) in radial distribution networks (RDNs) is solved in this paper while taking the growth of the load from electric vehicles (EVs) into consideration. This is accomplished by adapting an improved variant of the flower pollination algorithm (IFPA) with an enhanced local search capability. The primary objective of determining the locations and sizes of CBs is to minimize the distribution losses in the operation and control of RDNs. Additionally, the effect of CBs is shown by the increased net savings, greater voltage stability, and improved voltage profile. A voltage stability index (VSI) was used in the optimization process to determine the predefined search space for CB locations, and a double-direction learning strategy (DLS) was then considered to optimize the locations and sizes while maintaining a balance between the exploration and exploitation phases. Three IEEE RDNs were used to simulate various EV load increase scenarios as well as typical loading situations. According to a comparison with the literature, the IPFA produced global optimum results, and the proposed CBs allocation approach demonstrated enhanced performance in RDNs under all scenarios of EV load growth. 2022, University of Kragujevac, Faculty of Science. All Rights Reserved. -
Optimal Switching Operations of Soft Open Points in Active Distribution Network for Handling Variable Penetration of Photovoltaic and Electric Vehicles Using Artificial Rabbits Optimization
Global warming, rising fuel prices, and limited conventional fuel supplies are driving the use of renewable energy, battery energy storage, and electric vehicles, transforming traditional electrical distribution networks into active distribution networks. Stochastic technologies can present operational and control challenges, especially for radially configured active distribution networks. In this scenario, strengthening the existing active distribution networks is necessary. This study optimally integrates soft open points for dynamic network reconfiguration to handle uncertainty in active distribution networks. The location, size, and reconfiguration of the soft open points were obtained for the hourly load profile, which included electric vehicle fleet load penetration and PV distributed generation. The proposed multi-objective function uses active power loss, voltage profile, and reliability indices. The proposed multivariable optimization problem was solved using artificial rabbits optimization. The simulations were performed on a modified IEEE 33-bus radial distribution system. The computational efficiency of artificial rabbits optimization is competitive with other prominent algorithms. The proposed approach of optimal soft open points and dynamic network reconfiguration is utilized to cope with uncertainty and run the present active distribution networks with better technical and reliability characteristics. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Optimising lead qualification through machine learning: A customer data-driven approach
Lead generation is the process of turning an outside person or business into a customer of the business. Traditionally, marketing personnel must conduct significant follow-ups in order to convert even one potential consumer. Converting bad client leads can cause businesses to burn through cash reserves. As a result of this, it is now necessary to develop an automated system that can correctly anticipate whether or not a lead should be explored (converted to a customer or not). In this study, an attempt is made to evaluate historical data for leads produced by other businesses in order to train and validate a machine learning (ML)/deep learning (DL) model and test it against real-world characteristics to categorise them as hot leads (convert to customers) or cold leads (failed leads). This can be achieved by employing ML algorithms, low codeno code libraries, such as PyCaret in Python, and can be used to make predictions regarding probable lead creation, propensity to convert generated leads and optimal actions on the leads by communications teams. Supervised ML algorithms such as logistic regression, decision trees, random forests and other models using a Python library were built to score leads for identifying potential conversions. With good and broad lead-scoring models in place, businesses can optimise their CTI actions on the basis of lead prioritisation and let go of non-prospect leads at the right time to cut costs and enable efficiency. The result of this study reveals that 52 per cent of the sample of 74,779 leads are cold leads and 48 per cent are hot leads that are sales qualified. The leads are qualified using the lead score matrix. This method can aid digital businesses to remove unqualified leads and manage leads better, and therefore improve the quality of the leads sent to clients. This, in turn, will improve conversion rates for individual customers. These increased conversion rates will enhance the business strategy of digital marketing firms. Henry Stewart Publications. -
Optimising QoS with load balancing in cloud computing applying dual fuzzy technique
Cloud computing has become a necessity when the internet usage has increased drastically. This research paper objective is to optimise quality of service in cloud computing using dual fuzzy technique. With the competition to provide the best quality service at cloud data centre, we are analysing the parameters of average response time, average completion time, average CPU utilisation and job success. Cloud-sim simulator along with the mathematical model is used to provide reliable and valid result. To achieve the best result, the load in data centre needs to be efficiently distributed, so that it is managed to process maximum service requests with the best service response time and very few failures. In this paper, we applied dual fuzzy technique for the load balancing in the cloud data centre and the findings were extensive and support the proposed technique. With this technique, cloud computing service provider can provide better quality service. Copyright 2021 Inderscience Enterprises Ltd. -
Optimization and Design of a Sustainable Industrial Grid System
Electricity is a multifaceted form of energy and is used globally, with a continuously growing demand. Electrical power grids are there for more than 150 years. The generated electrical power is delivered to different industrial, commercial, and residential sectors, thereby fulfilling the ever-growing demand. In this research paper, the design and optimization of an industrial grid for various electrical loads is discussed. The electrical grid ensures a stable power supply to the loads by providing quality power with the minimum total harmonic distortion (THD) possible. A complete study of the short circuit current has been done in two different electrical grid systems, as it is seen that the short circuit current depends on the impedance of the transformer which feeds the load. These two designs of a single diagram will be simulated by using a power system analyzer, the Electrical Transient Analyzer Program (ETAP) software. The different electrical parameters, like choosing the optimised rated generator, cables, and transformers, are done. Load flow analysis is performed on both the design to evaluate the THD, short circuit fault, as well as to choose the right protection circuit for the system. 2022 Samat Iderus et al.