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Relevant bibliographies by topics / Swarna

Academic literature on the topic 'Swarna'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Swarna"

1

Saha, Indraneel, Bipul Sarkar, Arijit Ghosh, Arnab Kumar De, and M. K. Adak. "Physiological responses of sub1A QTL under induced dehydration stress for varying days in rice." Plant Science Today 7, no. 1 (February 6, 2020): 112–21. http://dx.doi.org/10.14719/pst.2020.7.1.654.

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This study analysed the rice genotype with sub1A quantitative trait loci that may interact with ongoing exposure of dehydration. cv. Swarna Sub1 had more nutrients efficiency with increased membrane permeability than cv. Swarna. sub1A QTL with its expression to water / osmotic deficit, however, indirectly to impede the oxidative stress under dehydration might help cv. Swarna Sub1. At maximum dehydration cv. Swarna Sub1 had 1.12 fold excess electrolyte leakages than cv. Swarna under dehydration. cv. Swarna Sub1 had better Nicotinamide adenine dinucleotide phosphate-malic enzyme activity to secure carbon dioxide exchange. A proportional increase in enzyme activity all through dehydration stress maximize under light in cv. Swarna Sub1. At maximum dehydration cv. Swarna Sub1 at saturating substrate concentration was increased by 1.12 fold than other cultivar. The ratio of glutathione (GSH:GSSG) more depleted in cv. Swarna Sub1 through the dehydration period. cv. Swarna could be more promising to retrieve the activity by 1.80 fold than cv. Swarna Sub1 under maximum dehydration. Dehydroascorbate reductase activity was also maintained in cv. Swarna with 1.20 fold ahead than cv. Swarna Sub1 under same condition. As a biomarker of oxidative stress cv. Swarna Sub1 appeared to be less sensitive with the loss of protein oxidation, however, recorded with 25% less carbonyl content than cv. Swarna. Both the genotypes had scored a significant sensitivity with tissue specific distribution for reactive oxygen species as detected by histochemical assay.

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2

PANDA, DEBABRATA, and RAMANI KUMAR SARKAR. "ROLE OF NON-STRUCTURAL CARBOHYDRATE AND ITS CATABOLISM ASSOCIATED WITH SUB 1 QTL IN RICE SUBJECTED TO COMPLETE SUBMERGENCE." Experimental Agriculture 48, no. 4 (May 17, 2012): 502–12. http://dx.doi.org/10.1017/s0014479712000397.

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SUMMARYThe present study is to characterise the non-structural carbohydrate (NSC) status and its catabolism along with elongation growth in rice cultivars either possessing or not possessing the Sub 1 quantitative trait locus (QTL), i.e. Swarna and Swarna Sub1 exposed to seven days of complete submergence. During submergence, Swarna accelerated the rate of stem and leaf elongation and rapidly consumed NSC. In contrast, Swarna Sub1 consumed energy resources more slowly and maintained similar growth rate to that of non-submerged plants. Swarna Sub1 showed better utilisation of carbohydrate than that of Swarna by progressive induction of alcohol dehydrogenase, starch phosphorylase and total and α-amylase enzyme activity during submergence. Overall, submergence tolerance conferred by the Swarna Sub1 QTL is correlated with better maintenance and utilisation of NSC than that of Swarna.

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3

Kumar Garg, Naresh, Dharmendra Sharma, Trilok chand, and Satynarayan bansal. "Immunomodulator Effect of Swarna Prashana(Oral Administration of Gold as Electuary) -A Conceptual Study." International Research Journal of Ayurveda & Yoga 05 (2022): 178–82. http://dx.doi.org/10.47223/irjay.2022.5628.

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Swarna Prashana(the oral administration of gold as an electuary) is a unique Ayurvedic treatment in the field of children. The canonical textbook of Kaumarabhritya(paediatrics), Kashyapa Samhita, illustrates this unusual formulation in the setting of Lehana (licking procedure by electuary). It has been explained that gold should be triturated with water, honey, and Ghrita on a pre-washed and clean stone facing east, and the semisolid concoction should be presented to the Shishu/infant. Swarna has been used in a variety of forms and doses throughout history, including raw gold, pure metal, gold foils, red colloidal solution, Swarna Patra, Swarna Parpati, and Bhasma,to name a few.Swarnaprashana is a practise in which Swarna and herbs are given to children orally in the form of a liquid, semi-solid, or paste. Madhu + Ghrita + Abharak bhasma + Swarna Bhasma + Swarnamakshik bhasma + Brahmi Churna + Kantakari +Shankhpushpi +Haritaki +Haridra + Kootha are the constituents of Swarna prashana. In Present Conceptual study describe the Immunomodulator effect of Swarna Prashanain detail according to Ayurveda.

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4

Koyande, Aishwarya Krishna, and Swati Shankar Gadgil. "PHARMACEUTICAL PREPARATION AND DETERMINATION OF QUALITY STANDARDS OF SARASWATARISHTA: A HERBO-MINERAL ALCOHOLIC FORMULATION." International Journal of Research in Ayurveda and Pharmacy 11, no. 5 (October 30, 2020): 88–93. http://dx.doi.org/10.7897/2277-4343.1105147.

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Saraswatarishta is a herbo-mineral hydro alcoholic formulation prescribed for various memory and sleep related disorders as well as a brain tonic. It is a unique formulation where the presence of swarna during the fermentation process may be responsible to manifold the action of remaining herbal drugs. Though the formulation is prescribed widely and available in the market with different varieties, there is a lack of documentation regarding the standard operating procedure and quality of the product. In the present study, Saraswatarishta and Swarna Saraswatarishta were prepared following standard operating procedure. Both the formulations were subjected to physico-chemical tests and values were found within the normal limits of arishta preparation from Ayurvedic Pharmacopeia of India. The sample of Swarna Saraswatarishta was analysed to check the presence of swarna using the ICP-MS technique. It was found below the level of quantification which interprets the absence of swarna in the formulation. This is the first and foremost report regarding the presence of Swarna in Swarna Saraswatarishta.

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5

Dash, S. R., B. K. Routray, S. K. Mohanty, and N. Behera. "Evaluation of excess water tolerant rice varieties Swarna sub-1 and CR-1009 sub -1 under Head to Head Project in East and South- Eastern Coastal Plain zone of Odisha." Current Agriculture Research Journal 8, no. 1 (March 26, 2020): 39–45. http://dx.doi.org/10.12944/carj.8.1.07.

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Flash flood or submergence is a common phenomenon in rice growing rainfed lowland areas that seriously affects crop establishment leading to severe yield losses. A few submergence–tolerant rice varieties have been developed by introgression SUB -1 gene into mega rice varieties of South Asia. Performance of this variety can be further enhanced through adoption of appropriate management practices. Farners Participatorty trials were conducted using Swarna- sub-1 and CR 1009 sub-1 during kharif, 2017 in three adopted villages at Erasama, Kujanga and Raghunathapur blocks of Jagatsinghpur district. Results of this experiment revealed that at recommended doses of N:P:K @ 80-40-40 Kgha-1, transplanting of 30 days aged seedlings and additional N-dose of 20kg/ha at 7 days after receding of flood water resulted in better post submergence recovery and maximum yield. Maximum number of tillers m-2 was recorded in CR 1009 sub -1 followed by Pooja and Swana Sub -1. Rice variety Swarna Sub –I rerecorded an yield of 55.23 q ha -1 which was 14.5% higher as compared to farmers variety Swrana 48.2 q ha -1. The Swarna Sub-1 and CR 1009 sub-1 survived in the flood and gave higher yield under submergence condition in the coastal flood prone area

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6

Munda, Sushmita, Sanjoy Saha, Totan Adak, Nitiprasad Jambhulkar, Priyadarsini Sanghamitra, and Bhaskar Chandra Patra. "Performance of cultivated indica rice (Oryza Sativa L.) as affected by weedy rice." Experimental Agriculture 55, no. 6 (January 31, 2019): 875–84. http://dx.doi.org/10.1017/s0014479718000455.

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AbstractWeedy rice species exhibit differential competitive ability and cause significant losses to rice yield. The present study was conducted to evaluate the competing ability of weedy rice accessions collected from foothills of eastern Himalayas and coastal Odisha grown in the presence of cultivated indica rice var. Swarna. The competitive ability of Swarna and weedy rices were estimated on the basis of (i) Swarna yield reduction under different durations of competition with weedy rice; and (ii) nutrient uptake by Swarna and weedy rice in competitive environment. There was significant yield reduction (18%) when competition was allowed until 6 weeks after emergence (WAE) compared to competition until 2 WAE, which was due to vigorous growth of weedy rice at early vegetative stage. The biomass accumulation and tiller number of weedy rice were significantly higher compared to Swarna. Odisha weedy rice accession recorded about 18, 57 and 24% higher N, P and K uptake, respectively, than Swarna. The highest yield reduction (22%) in Swarna was recorded when grown with OA1 and the lowest impact (7.7%) was recorded with AA2. As conclusion, management practices should be implemented within 2–4 weeks of emergence considering 5–10% acceptable yield loss of Swarna, and grain yield of cultivated rice was reduced significantly by high N and K uptake by weedy rice under a competitive environment.

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Pesta Gultom, Pesta, and Claudie. "Pengaruh Kompetensi Kewirausahaan dan Pengawasan Manajemen terhadap Perkembangan Perusahaan PT Ekuator Swarna Sekuritas Cabang Medan." Jurnal Manajemen Bisnis Eka Prasetya : Penelitian Ilmu Manajemen 6, no. 2 (September 18, 2020): 46–58. http://dx.doi.org/10.47663/jmbep.v6i2.56.

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This study aims to determine how much influence Entrepreneurship Competence and Management Supervision on the Development of PT Ekuator Swarna Sekuritas Medan Branch. The research methodology used is descriptive and explanatory survey methods. The population in this study were 1003 customers of PT Ekuator Swarna Sekuritas Medan Branch. The technique of determining the number of samples in this study was determined using the Slovin formula and amounted to 91 respondents. The t test results show that the Entrepreneurial Competence variable is positively and significantly influenced by the Development of PT Ekuator Swarna Sekuritas Medan Branch and the Management Control variable has a positive and significant effect on the development of PT Ekuator Swarna Sekuritas Medan Branch. Simultaneously affect the dependent variable (Company Development)

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Chandrasekar, Saranya, Purushothaman Natarajan, Priyank Hanuman Mhatre, Mahesh Mahajan, Sundararaj Nivitha, Venkatasalam E. Palanisamy, Umesh K. Reddy, and Palanisamy Sundararaj. "RNA-Seq of Cyst Nematode Infestation of Potato (Solanum tuberosum L.): A Comparative Transcriptome Analysis of Resistant and Susceptible Cultivars." Plants 11, no. 8 (April 7, 2022): 1008. http://dx.doi.org/10.3390/plants11081008.

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Potato (Solanum tuberosum L.) is an important food crop worldwide, and potato cyst nematodes (PCNs) are among the most serious pests. The identification of disease resistance genes and molecular markers for PCN infestation can aid in crop improvement research programs against PCN infestation. In the present study, we used high-throughput RNA sequencing to investigate the comprehensive resistance mechanisms induced by PCN infestation in the resistant cultivar Kufri Swarna and the susceptible cultivar Kufri Jyoti. PCN infestation induced 791 differentially expressed genes in resistant cultivar Kufri Swarna, comprising 438 upregulated and 353 downregulated genes. In susceptible cultivar Kufri Jyoti, 2225 differentially expressed genes were induced, comprising 1247 upregulated and 978 downregulated genes. We identified several disease resistance genes (KIN) and transcription factors (WRKY, HMG, and MYB) that were upregulated in resistant Kufri Swarna. The differentially expressed genes from several enriched KEGG pathways, including MAPK signaling, contributed to the disease resistance in Kufri Swarna. Functional network analysis showed that several cell wall biogenesis genes were induced in Kufri Swarna in response to infestation. This is the first study to identify underlying resistance mechanisms against PCN and host interaction in Indian potato varieties.

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Ghritlahre, S. K., Mahesh Rao, S. L. Pavani, Vineeta Singh, U. S. Singh, Sandhya Sandhya, and P. K. Singh. "Estimation of yield and grain qualities of marker assisted backcross derived lines of submergence rice against sheath blight disease." Journal of Applied and Natural Science 9, no. 2 (June 1, 2017): 1042–48. http://dx.doi.org/10.31018/jans.v9i2.1319.

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Sheath blight caused by Rhizoctonia solani is one of the most devastating diseases of rice (Oryza sativa) and causes enormous yield losses over the world after blast, the disease can cause yield loss upto 50 per cent in advanced stage and adversely affects quality of straw. Breeding for resistant varieties is the only viable option to combat the disease efficiently. In this study, our findings showed a significant increase in number of spikelet’s per panicle (3.45 %), test weight (0.62 %) and grain yield (0.72 %) compared to recurrent parent Swarna sub-1. The range of mean performance of 18 BC2F1 selected improved lines varied for per cent disease severity from 26.75 to 43.58 at 16 days after inoculation. Among the 18 improved lines, only four lines (Swarna sub-1-6, Swarna sub-1-32, Swarna sub-1-13 and Swarna sub-1-29) showed resistance score of 1-3. The remaining fourteen lines showed moderate resistance with a score of 3-5. Hence, the resistance line could be exploited in sheath blight resistance breeding programme and the same line can also be released as a variety against sheath blight of rice after testing over multilocation trails.

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Selywati, Selywati, Syawaluddin Syawaluddin, Aidil Putra, and Thomas Sumarsan Sumarsan Goh. "Pengaruh Kompetensi dan Insentif Terhadap Prestasi Kerja Karyawan PT. Kartika Swarna Dwipa." Jesya 6, no. 1 (January 1, 2023): 156–65. http://dx.doi.org/10.36778/jesya.v6i1.892.

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Kartika Swarna Dwipa is a company engaged in the marketing of Tupperware products. Employee work performance is starting to experience a decline which is indicated by the low morale of employees in completing work. Low competence because employees tend to only graduate from high school and have not been given training. The provision of incentives in fact has problems such as commissions and overtime incentives that are not as promised. This research uses quantitative descriptive research. The total population in the study was 39 employees with a sampling technique using saturated samples. Simultaneously there is a significant influence between the variables of competence and incentives on the work performance of employees of PT. Kartika Swarna Dwipa. While partially, it was found that the competence variable had a significant effect on the work performance of PT. Kartika Swarna Dwipa. Partially, it was found that the incentive variable had a significant effect on the work performance of PT. Kartika Swarna Dwipa. The results of this study indicate that the competence variable has more influence than incentive on the work performance of PT. Kartika Swarna Dwipa.

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Dissertations / Theses on the topic "Swarna"

1

Agarwal, Swarna [Verfasser], and Heinrich [Akademischer Betreuer] Planck. "Nanoskalig strukturierte Textilfiltermedien für die Trennung von Öl-Wasser-Emulsionen / Swarna Agarwal. Betreuer: Heinrich Planck." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2012. http://d-nb.info/1021923419/34.

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McNabb, Andrew W. "Parallel Particle Swarm Optimization and Large Swarms." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2480.

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Optimization is the search for the maximum or minimum of a given objective function. Particle Swarm Optimization (PSO) is a simple and effective evolutionary algorithm, but it may take hours or days to optimize difficult objective functions which are deceptive or expensive. Deceptive functions may be highly multimodal and multidimensional, and PSO requires extensive exploration to avoid being trapped in local optima. Expensive functions, whose computational complexity may arise from dependence on detailed simulations or large datasets, take a long time to evaluate. For deceptive or expensive objective functions, PSO must be parallelized to use multiprocessor systems and clusters efficiently. This thesis investigates the implications of parallelizing PSO and in particular, the details of parallelization and the effects of large swarms. PSO can be expressed naturally in Google's MapReduce framework to develop a simple and robust parallel implementation that automatically includes communication, load balancing, and fault tolerance. This flexible implementation makes it easy to apply modifications to the algorithm, such as those that improve optimization of difficult objective functions and improve parallel performance. Results show that larger swarms help with both of these goals, but they are most effective if arranged into sparse topologies with lower overhead from communication. Additionally, PSO must be modified to use communication more efficiently in a large sparse swarm for objective functions where information ideally flows quickly through a large swarm. Swarm size is usually fixed at a modest number around 50, but particularly in a parallel computational environment, much larger swarms are much more effective for deceptive objective functions. Likewise, swarms much smaller than 50 are more effective for expensive but less deceptive functions. In general, swarm size should be carefully chosen using all available information about the objective function and computational environment.

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Riyaz, Firasath Maurer Peter M. Marks Robert J. "Evolving a Disjunctive Predator Prey Swarm using PSO Adapting Swarms with Swarms/." Waco, Tex. : Baylor University, 2005. http://hdl.handle.net/2104/1465.

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Ashcraft, C. Chace. "Moderating Influence as a Design Principle for Human-Swarm Interaction." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7406.

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Robot swarms have recently become of interest in both industry and academia for their potential to perform various difficult or dangerous tasks efficiently. As real robot swarms become more of a possibility, many desire swarms to be controlled or directed by a human, which raises questions regarding how that should be done. Part of the challenge of human-swarm interaction is the difficulty of understanding swarm state and how to drive the swarm to produce emergent behaviors. Human input could inhibit desirable swarm behaviors if their input is poor and has sufficient influence over swarm agents, affecting its overall performance. Thus, with too little influence, human input is useless, but with too much, it can be destructive. We suggest that there is some middle level, or interval, of human influence that allows the swarm to take advantage of useful human input while minimizing the effect of destructive input. Further, we propose that human-swarm interaction schemes can be designed to maintain an appropriate level of human influence over the swarm and maintain or improve swarm performance in the presence of both useful and destructive human input. We test this theory by implementing a piece of software to dynamically moderate influence and then testing it with a simulated honey bee colony performing nest site selection, simulated human input, and actual human input via a user study. The results suggest that moderating influence, as suggested, is important for maintaining high performance in the presence of both useful and destructive human input. However, while our software seems to successfully moderate influence with simulated human input, it fails to do so with actual human input.

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Nagavalli, Sasanka. "Algorithms for Timing and Sequencing Behaviors in Robotic Swarms." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1215.

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Robotic swarms are multi-robot systems whose global behavior emerges from local interactions between individual robots and spatially proximal neighboring robots. Each robot can be programmed with several local control laws that can be activated depending on an operator’s choice of global swarm behavior (e.g. flocking, aggregation, formation control, area coverage). In contrast to other multi-robot systems, robotic swarms are inherently scalable since they are robust to addition and removal of members with minimal system reconfiguration. This makes them ideal for applications such as search and rescue, environmental exploration and surveillance. Practical missions often require a combination of swarm behaviors and may have dynamically changing mission goals. However, a robotic swarm is a complex distributed dynamical system, so its state evolution depends on the timing as well as sequence of the supervisory inputs. Thus, it is difficult to predict the effects of an input on the state evolution of the swarm. More specifically, after becoming aware of a change in mission goals, it is unclear at what time a supervisory operator must convey this information to the swarm or which combination of behaviors to use to accomplish the new goals. The main challenges we address in this thesis are characterizing the effects of input timing on swarm performance and using this theory to inform automated composition of swarm behaviors to accomplish updated mission goals. We begin by formalizing the notion of Neglect Benevolence — the idea that delaying the application of an input can sometimes be beneficial to overall swarm performance — and using the developed theory to demonstrate experimentally that humans can learn to approximate optimal input timing. In an adversarial setting, we also demonstrate that by altering only the timing of consensus updates for a subset of the swarm, we can influence the agreement point of the entire swarm. Given a library of swarm behaviors, automated behavior composition consists of identifying a behavior schedule that must specify (1) the appropriate sequence of behaviors and (2) the corresponding duration of execution for each behavior. Applying our notion of Neglect Benevolence, it is clear these two parts are intricately interdependent. By first assuming the durations are known, we present an algorithm to identify the optimal behavior sequence to achieve a desired swarm mission goal when our library contains general swarm behaviors. By restricting our library to consensus-based swarm behaviors, we then relax the assumption on known durations and present an algorithm to simultaneously find the sequence and durations of swarm behaviors to time-optimally accomplish multiple unordered goals.

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Grosh, John Rolfes. "Multi-Human Management of a Hub-Based Colony: Efficiency and Robustness in the Cooperative Best M-of-N Task." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8544.

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Swarm robotics is an emerging field that is expected to provide robust solutions to spatially distributed problems. Human operators will often be required to guide a swarm in the fulfillment of a mission. Occasionally, large tasks may require multiple spatial swarms to cooperate in their completion. We hypothesize that when latency, bandwidth, operator dropout, and communication noise are significant factors, human organizations that promote individual initiative perform more effectively and resiliently than hierarchies in the cooperative best-m-of-n task. Simulations automating the behavior of hub-based swarm robotic agents and groups of human operators are used to evaluate this hypothesis. To make the comparisons between the team and hierarchies meaningful, we explore parameter values determining how simulated human operators behave in teams and hierarchies to optimize the performance of the respective organizations. We show that simulation results generally support the hypothesis with respect to the effect of latency and bandwidth on organizational performance.

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Grandi, Raffaele <1976&gt. "Coordination and Control of Autonomous Mobile Robots Swarms by using Particle Swarm Optimization Algorithm and Consensus Theory." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5904/1/Grandi_Raffaele_tesi.pdf.

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This thesis presents some different techniques designed to drive a swarm of robots in an a-priori unknown environment in order to move the group from a starting area to a final one avoiding obstacles. The presented techniques are based on two different theories used alone or in combination: Swarm Intelligence (SI) and Graph Theory. Both theories are based on the study of interactions between different entities (also called agents or units) in Multi- Agent Systems (MAS). The first one belongs to the Artificial Intelligence context and the second one to the Distributed Systems context. These theories, each one from its own point of view, exploit the emergent behaviour that comes from the interactive work of the entities, in order to achieve a common goal. The features of flexibility and adaptability of the swarm have been exploited with the aim to overcome and to minimize difficulties and problems that can affect one or more units of the group, having minimal impact to the whole group and to the common main target. Another aim of this work is to show the importance of the information shared between the units of the group, such as the communication topology, because it helps to maintain the environmental information, detected by each single agent, updated among the swarm. Swarm Intelligence has been applied to the presented technique, through the Particle Swarm Optimization algorithm (PSO), taking advantage of its features as a navigation system. The Graph Theory has been applied by exploiting Consensus and the application of the agreement protocol with the aim to maintain the units in a desired and controlled formation. This approach has been followed in order to conserve the power of PSO and to control part of its random behaviour with a distributed control algorithm like Consensus.

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Grandi, Raffaele <1976&gt. "Coordination and Control of Autonomous Mobile Robots Swarms by using Particle Swarm Optimization Algorithm and Consensus Theory." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5904/.

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This thesis presents some different techniques designed to drive a swarm of robots in an a-priori unknown environment in order to move the group from a starting area to a final one avoiding obstacles. The presented techniques are based on two different theories used alone or in combination: Swarm Intelligence (SI) and Graph Theory. Both theories are based on the study of interactions between different entities (also called agents or units) in Multi- Agent Systems (MAS). The first one belongs to the Artificial Intelligence context and the second one to the Distributed Systems context. These theories, each one from its own point of view, exploit the emergent behaviour that comes from the interactive work of the entities, in order to achieve a common goal. The features of flexibility and adaptability of the swarm have been exploited with the aim to overcome and to minimize difficulties and problems that can affect one or more units of the group, having minimal impact to the whole group and to the common main target. Another aim of this work is to show the importance of the information shared between the units of the group, such as the communication topology, because it helps to maintain the environmental information, detected by each single agent, updated among the swarm. Swarm Intelligence has been applied to the presented technique, through the Particle Swarm Optimization algorithm (PSO), taking advantage of its features as a navigation system. The Graph Theory has been applied by exploiting Consensus and the application of the agreement protocol with the aim to maintain the units in a desired and controlled formation. This approach has been followed in order to conserve the power of PSO and to control part of its random behaviour with a distributed control algorithm like Consensus.

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9

Gazi, Veysel. "Stability Analysis of Swarms." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1029812963.

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10

Abdenebaoui, Larbi [Verfasser], Hans-Jörg [Akademischer Betreuer] [Gutachter] Kreowski, and Jürgen [Gutachter] Pannek. "Graph-Transfromational Swarms : A Graph-Transformational Approach to Swarm Computation / Larbi Abdenebaoui ; Gutachter: Hans-Jörg Kreowski, Jürgen Pannek ; Betreuer: Hans-Jörg Kreowski." Bremen : Staats- und Universitätsbibliothek Bremen, 2016. http://d-nb.info/112770141X/34.

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Books on the topic "Swarna"

1

swarna kumar. 3rd ed. Wiesbaden: Bauverlag, 1985.

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Bharti, Prakash. Baal patrakarta swarna yug ki or. Delhi: Parmeshbri, 1993.

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Tripathi, Vachnesh. Bhoole-Bisre Krantikari: Swadhinta ki swarna-jayanti. Dehli: Premi Prakashan Mandir, 1997.

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CENWOR (Organization : Sri Lanka) and Sri Lanka Association for the Advancement of Education, eds. Envisioning education for all, gender equality: Selected publications of Professor Swarna Jayaweera : a felicitation volume. Colombo: Centre for Women's Research (CENWOR) & Sri Lanka Association for the Advancement of Education, 2014.

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Sarma, K. S. R. N., Banerji Pranab, and Indian Institute of Public Administration., eds. State and urban poor: Case of SJSRY in India. New Delhi: Indian Institute of Public Administration, 2004.

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MILAN, Imdadul Huq. Swapna. Dhaka: Shikha, 1989.

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Mohiuddin, Mohammad. Premer swarga. Dhaka: Asia, 2002.

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Roy, Jayanta. "B-swarga" (:). New Delhi: Nature Morte, 2009.

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KAR, Bimal. Swarga khelna. Calcutta: Karnini, 1988.

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Gangopadhyay, Sunil. Swapna Basandatta. Calcutta: Sangbad, 1992.

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Book chapters on the topic "Swarna"

1

Nagamani, K. T., S. S. Chethana, and T. N. Bhagwat. "Precipitation and Stream flow Trends for Swarna River Watershed, Karnataka, India." In Climate Change Impact on Water Resources, 345–59. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8524-9_28.

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Kordon, Arthur K. "Swarm Intelligence: The Benefits of Swarms." In Applying Computational Intelligence, 145–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-69913-2_6.

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Chen, Guochu, and Jinshou Yu. "Two Sub-swarms Particle Swarm Optimization Algorithm." In Lecture Notes in Computer Science, 515–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539902_63.

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Kazadi, Sanza. "Using Swarm Engineering to Design Physicomimetic Swarms." In Physicomimetics, 529–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22804-9_17.

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Alinezhad, Alireza, and Javad Khalili. "SWARA Method." In New Methods and Applications in Multiple Attribute Decision Making (MADM), 99–102. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15009-9_14.

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Xia, Lirong, Jizheng Chu, and Zhiqiang Geng. "A New Multi-Swarms Competitive Particle Swarm Optimization Algorithm." In Lecture Notes in Electrical Engineering, 133–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-26001-8_18.

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Röhler, Antonio Bolufé, and Stephen Chen. "An Analysis of Sub-swarms in Multi-swarm Systems." In AI 2011: Advances in Artificial Intelligence, 271–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25832-9_28.

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Shiva, Vandana. "Swaraj." In The Wiley Blackwell Companion to Religion and Ecology, 12–19. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118465523.ch2.

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Crane, Ralph J. "Swaraj." In Inventing India, 100–135. London: Palgrave Macmillan UK, 1992. http://dx.doi.org/10.1057/9780230380080_5.

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Jahanbegloo, Ramin. "Swaraj." In Gandhi and the Idea of Swaraj, 29–42. London: Routledge India, 2023. http://dx.doi.org/10.4324/9781003366492-3.

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Conference papers on the topic "Swarna"

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Alam, Md Anjir, Iftikharun Nisha, Md Mahadi Hasan, and A. K. M. Nazrul Islam. "Designing of an Underwater Power Transmission System and Hybrid Power Supply for an Isolated Island of Bangladesh: Swarna Dwip." In 2021 International Conference on Science & Contemporary Technologies (ICSCT). IEEE, 2021. http://dx.doi.org/10.1109/icsct53883.2021.9642504.

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Vishwakarma, Siddharth, Chandrakant Genu Dalbhagat, and Hari Niwas Mishra. "Food-to-Food Fortification of Rice Flour (Swarna Cv.) Using Dried Basil, Marjoram, and Spearmint Leaves Powders: A Physicochemical and Nutritional Study." In Foods 2021. Basel Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/foods2021-10947.

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Kalivarapu, Vijay K., and Eliot H. Winer. "Parallel Implementation of Particle Swarm Optimization (PSO) Through Digital Pheromone Sharing." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49444.

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In this paper, a parallelization model for PSO through sharing of digital pheromones between multiple particle swarms to search n-dimensional design spaces is presented. Digital pheromones are models simulating real pheromones produced by insects for communication to indicate a source of food or a nesting location. Particle swarms search the design space with digital pheromones aiding communication within the swarm to improve search efficiency. Digital pheromones have demonstrated the capability of searching design spaces within PSO in the previous work by authors in both single and coarse granular parallel computing environments. Multiple swarms are simultaneously deployed across various processors in the coarse granular scheme and synchronization is carried out only when all swarms achieved convergence. This was done in an effort to reduce processor-to-processor communication and network latencies. With an appropriate parallelization scheme, the benefits of digital pheromones and swarm communication can potentially outweigh the network latencies resulting in improved search efficiency and accuracy. A swarm is deployed in the design space across different processors to explore this idea. Each part of the swarm is made to communicate with each other through an additional processor. Digital pheromones aiding within a swarm, communication between swarms is facilitated through the developed parallelization model. In this paper, the development and implementation of this method together with benchmarking test cases are presented.

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Morikawa, Koichi, Shigeyuki Urano, Toshiyuki Sanada, and Takayuki Saito. "Energy Spectral Properties Due to Bubble-Liquid Interaction in Oscillating-Grid Turbulence." In 16th International Conference on Nuclear Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/icone16-48470.

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In the present study, liquid-phase turbulence modulation induced by bubble swarms ascending in arbitrary turbulence is experimentally investigated. Liquid-phase homogeneous isotropic turbulence is formed using an oscillating grid in a cylindrical acrylic vessel of 149 mm in inner diameter. A bubble swarm of 19 bubbles is examined. The bubble size and generating time are completely controlled applying a bubble generating device using audio speakers. The bubble swarms are generated at a frequency of 4 Hz. The motion of individual bubbles of the bubble swarm is captured by a high-speed video camera. The liquid phase motion is also measured by PIV. The turbulence intensity, spatial correlation, integral scale and energy spectrum are calculated from the PIV data. It is confirmed that the oscillating-grid turbulence is an isotropic turbulence from the PIV result. When the bubble swarm is added, the original isotropic turbulence is modulated to an anisotropic turbulence by the mutual interaction between the bubble swarm and ambient isotropic turbulence. Moreover the bubble motion is notably changed. The bubble swarm contributes an increase of the energy spectrum to the ambient oscillating-grid turbulence. Moreover the suppression of energy spectrum occurred by interaction between bubble swarm and oscillating-grid turbulence compared with the energy spectrum in the flow induced by bubble swarms.

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Kumar, Ajitabh. "Joint Optimization of Well Placement and Control Using Multi-Stage, Multi-Swarm PSO." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22045-ms.

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Abstract Evolutionary optimization algorithms, including particle swarm optimization (PSO), have been successfully applied in oil industry for production planning and control. Such optimization studies are quite challenging due to large number of decision variables, production scenarios, and subsurface uncertainties. In this work, multi-stage, multi-swarm PSO (MS2PSO) algorithm is proposed to fix certain issues with canonical PSO algorithm such as premature convergence, excessive influence of global best solution, and oscillation. Multiple experiments are conducted using Olympus benchmark to compare the efficacy of algorithms. Results from canonical PSO are first compared with two PSO variations in which hyperparameters are tuned to prioritize exploration in early phase and exploitation in late phase. Firstly, linearly decreasing inertia weight (LDIW-PSO) is used to have greater weight of current particle position during initial iterations, and vice versa. Then, time-varying acceleration coefficients (TVAC-PSO) are used to have greater weight of personal best and lesser weight of global best during the initial iterations, and vice versa. Next, a two-stage multi-swarm PSO (2SPSO) is used where multiple-swarms of the first stage collapse into a single swarm in the second stage. Finally, MS2PSO with multiple stages and multiple swarms is used in which swarms recursively collapse after each stage. Multiple swarm strategy ensures that diversity is retained within the population and multiple modes are explored. Staging ensures that local optima found during initial stage does not lead to premature convergence. Optimization test case comprises of 90 control variables of which 72 are well control related and 18 are well placement related. Swarm intelligence refers to global patterns emerging from simple interactions among population. Algorithmic rules at micro level lead to social interaction at meso level, which then further leads to collective behavior at macro level. It is observed that different algorithm designs have their own benefits and drawbacks. Decreasing inertia weight in LDIW-PSO enables exploration in early stages and convergence around global best in the later stages. TVAC-PSO on the other hand restricts social learning and aids exploration in the early iterations. Social learning component is increased as run progresses, and population moves towards global best. 2SPSO aids in exploring multi-modal objective space, thus preventing premature convergence to a local optima. Swarms collapse into one group in the second stage, and run finally converges towards global best. Multiple swarms and stages in MS2PSO ensure that diversity in population is maintained throughout the run which enables continuous learning, and thus mitigates premature convergence. Both 2SPSO and MS2PSO are found to be helpful for problems with high dimensions and multiple modes where greater degree of exploration is desired. Commercial cloud computing and parallel programming were used to handle high computational workload and reduce run-time from weeks to days. Coefficients of canonical PSO are tuned in LDIW-PSO and TVAC-PSO, which helps in mitigating issues like premature convergence and oscillation. Two-stage PSO (2SPSO) where multiple swarms of first stage collapse into one in second stage, and multi-stage multi-swarm PSO (MS2PSO) where swarms recursively collapse into one are proposed. These algorithms modify the social behavior at meso scale based on number of swarms, number of stages and iterations in each stage.

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Schumann, Hans, Louis Rosenberg, and Gregg Willcox. "'"Human Swarms” of novice sports fans beat professional handicappers when forecasting NFL football games." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003287.

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The biological phenomenon of Swarm Intelligence (SI) enables social species to converge on group decisions by interacting in real-time systems. Studied in schools of fish, bee swarms, and bird flocks, biologists have shown for decades that SI can greatly amplify group intelligence in natural systems. Artificial Swarm Intelligence (ASI) is a computer-mediated technique developed in 2015 to enable networked human groups to form real-time systems that can deliberate and converge on decisions, predictions, estimations, and prioritizations. A unique combination of real-time HCI methods and AI algorithms, ASI technology (also called “Human Swarming” or “Swarm AI”) has been shown in many studies to amplify group intelligence in forecasting tasks, often enabling small groups of non-professionals to exceed expert level performance. In the current study, small groups of approximately 24 amateur sports fans used an online platform called Swarm to collaboratively make weekly predictions (against the spread) of every football game in four consecutive NFL seasons (2019 - 2022) for a total of 1027 forecasted games. Approximately 5 games per week (as forecast by the human swarm) were identified as “predictable” using statistical heuristics. Performance was compared against the Vegas betting markets and measured against accepted performance benchmarks for professional handicappers. It is well known that professional bettors rarely achieve more than 55% accuracy against the Vegas spread and that top experts in the world rarely exceed 58% accuracy. In this study the amateur sports fans achieved 62.5% accuracy against the spread when connected as real-time “swarms.” A statistical analysis of this result (across 4 NFL seasons) found that swarms outperformed the 55% accuracy benchmark for human experts with significance (p=0.002). These results confirmed for the first time that groups of amateurs, when connected in real-time using ASI, can consistently generate forecasts that exceeded expert level performance with a high degree of statistical certainty.Keywords: Swarm Intelligence, Artificial Swarm Intelligence, Collective Intelligence, Wisdom of Crowds, Hyperswarms,

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Go¨z, Manfred F., Martin Sommerfeld, Bernard Bunner, and Gretar Tryggvason. "Microstructure of a Bidisperse Swarm of Spherical Bubbles." In ASME 2002 Joint U.S.-European Fluids Engineering Division Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/fedsm2002-31395.

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Three-dimensional simulations of bidisperse bubble swarms rising in a liquid have been carried out. This article describes the microstructure of a swarm of mostly spherical bubbles representing 6% void fraction. The swarm consists of an equal number of large and small bubbles with volume ratio 2. While the behavior of the large bubbles is similar to that in a comparable monodisperse system, the behavior of the small bubbles is different.

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Edwards, Victoria, Paulo Rezeck, Luiz Chaimowicz, and M. Ani Hsieh. "Segregation of Heterogeneous Robotics Swarms via Convex Optimization." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9653.

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The division of labor amongst a heterogeneous swarm of robots increases the range and sophistication of the tasks the swarm can accomplish. To efficiently execute a task the swarm of robots must have some starting organization. Over the past decade segregation of robotic swarms has grown as a field of research drawing inspiration from natural phenomena such as cellular segregation. A variety of different approaches have been undertaken to devise control methods to organize a heterogeneous swarm of robots. In this work, we present a convex optimization approach to segregate a heterogeneous swarm into a set of homogeneous collectives. We present theoretical results that show our approach is guaranteed to achieve complete segregation and validate our strategy in simulation and experiments.

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Shang, Beining, Richard M. Crowder, and Klaus-Peter Zauner. "An Approach to Sorting Swarm Robots to Optimize Performance." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59984.

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Swarm robotic systems can offer many advantages including robustness, flexibility and scalability. However one of the issues relating to overall swarm performance that needs to be considered is hardware variations inherent in the implementation of individual swarm robots. This variation can bring behavioral diversity within the swarm, resulting in uncontrollable swarm behaviors, low efficiency, etc. If swarm robots could be separated by behaviors, operational advantages could be obtained. In this paper we report an approach to the sorting of large robotic swarms using an approach inspired by chromatography. Hence the tedious and expensive calibration process can be avoided. The results investigate the influence of the internal control parameters, together with environmental effects on the robotic behavioral sorting. We concluded that if the robot has knowledge of previous events coupled with a specific arena pattern density will offer improved behavioral sorting.

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Lomuscio, Alessio, and Edoardo Pirovano. "Verifying Fault-Tolerance in Probabilistic Swarm Systems." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/46.

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We present a method for reasoning about fault-tolerance in unbounded robotic swarms. We introduce a novel semantics that accounts for the probabilistic nature of both the swarm and possible malfunctions, as well as the unbounded nature of swarm systems. We define and interpret a variant of probabilistic linear-time temporal logic on the resulting executions, including those arising from faulty behaviour by some of the agents in the swarm. We specify the decision problem of parameterised fault-tolerance, which concerns determining whether a probabilistic specification holds under possibly faulty behaviour. We outline a verification procedure that we implement and use to study a foraging protocol from swarm robotics, and report the experimental results obtained.

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Reports on the topic "Swarna"

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Marks, David R. Mute Swans. U.S. Department of Agriculture, Animal and Plant Health Inspection Service, February 2018. http://dx.doi.org/10.32747/2018.7208745.ws.

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Mute swans (Cygnus olor) are an invasive species originally brought to the United States in the late 19th and early 20th centuries for ornamental ponds and lakes, zoos and aviculture collections. Original populations were located in northeastern states along the Hudson Valley but have since expanded to several Midwestern states and portions of the western U.S. and Canada. Mute swan damage includes competing with native waterfowl, destroying native plants, spreading disease, and colliding with aircraft. They are also considered a nuisance in some areas due to their abundant fecal droppings and aggressiveness towards people. Some have questioned the status of mute swans as an introduced species, but multiple reviews by scientists and the U.S. Fish and Wildlife Service clearly support the conclusion that mute swans are not native to North America. The Migratory Bird Treaty Act, therefore, does not protect mute swans, and management authority falls under jurisdiction of the states and Tribes.

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Greenspan, E. SWANS: A Prototypic SCALE Criticality Sequence for Automated Optimization Using the SWAN Methodology. Office of Scientific and Technical Information (OSTI), January 2001. http://dx.doi.org/10.2172/777696.

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Ungar, Eugene D., Montague W. Demment, Uri M. Peiper, Emilio A. Laca, and Mario Gutman. The Prediction of Daily Intake in Grazing Cattle Using Methodologies, Models and Experiments that Integrate Pasture Structure and Ingestive Behavior. United States Department of Agriculture, July 1994. http://dx.doi.org/10.32747/1994.7568789.bard.

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This project addressed the prediction of daily intake in grazing cattle using methodologies, models and experiments that integrate pasture structure and ingestive behavior. The broad objective was to develop concepts of optimal foraging that predicted ingestive behavior and instantaneous intake rate in single and multi-patch environments and extend them to the greater scales of time and space required to predict daily intake. Specific objectives included: to determine how sward structure affects the shape of patch depletion curves, to determine if the basic components of ingestive behavior of animals in groups differs from animals alone, and to evaluate and modify our existing models of foraging behavior and heterogeneity to incorporate larger scales of time and space. Patch depletion was found to be predominantly by horizon, with a significant decline in bite weight during horizon depletion. This decline derives from bite overlap, and is more pronounced on taller swards. These results were successfully predicted by a simple bite placement simulator. At greater spatial scales, patch selection was aimed at maximizing daily digestible intake, with the between patch search pattern being non-random. The processes of selecting a feeding station and foraging at a feeding station are fundamentally different. The marginal value theorem may not be the most appropriate paradigm for predicting residence time at a feeding station. Basic components of ingestive behavior were unaffected by the presence of other animals. Our results contribute to animal production systems by improving our understanding of the foraging process, by identifying the key sward parameters that determine intake rate and by improving existing conceptual and quantitative models of foraging behavior across spatial and temporal scales.

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D'Andrea, Raffaello. Control of Air Vehicle Swarms. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada530333.

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Turo, Laura A., Daniel C. Skorski, Michael J. Schweiger, and Joseph V. Ryan. Gleenglass Swarf Research. Office of Scientific and Technical Information (OSTI), April 2010. http://dx.doi.org/10.2172/1000821.

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Thompson, James. Spectra Swarm Evaluation. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1765853.

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Joshi, Rohan. Pendanaan Publik untuk Sekolah Swasta Berbiaya Rendah. Jakarta, Indonesia: Center for Indonesian Policy Studies, 2018. http://dx.doi.org/10.35497/270461.

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Fahrig, W. F., and T. D. West. Diabase dyke swarms of the Canadian Shield. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/133917.

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Ouellett, Nicholas T. Laboratory and Modeling Studies of Insect Swarms. Fort Belvoir, VA: Defense Technical Information Center, February 2016. http://dx.doi.org/10.21236/ad1010208.

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Madey, Gregory. Dynamic Predictive Simulations of Agent Swarms (DDDAS). Fort Belvoir, VA: Defense Technical Information Center, April 2014. http://dx.doi.org/10.21236/ada601979.

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