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Pulses are essential components of vegetarian diet and play a pivotal role in addressing malnutrition by providing a vital source of dietary protein. However, weed competition remains as a significant obstacle to blackgram (Vigna mungo L) production, resulting in 25 to 35% yield losses. A field experiment was carried out at the National Pulses Research Centre, Tamil Nadu Agricultural University, Vamban, Pudukkottai, India during Kharif seasons of 2020 and 2021, to assess the suitable post-emergence herbicides for appropriate weed control in blackgram grown under irrigated conditions. The experimental field was observed to contain notable grass weed flora, specifically Dactyloctenium aegyptium and Chloris barbata, as well as broadleaved weeds, such as Flaveria australica, Cleome gynandra, Eclipta alba, Convolvulus arvensis, Digera arvensis, Vicia spp., and Celosia argentea. The results demonstrated that among the chemical weed management methods, spraying of Fomesafen @ 220 g + Fluzifop p-butyl @ 220 g ha−1 at 20 days after sowing (DAS) as a post-emergence herbicide treatment exhibited superior weed control efficiency, recording 66.80% and 68.53% at 30 and 45 DAS, respectively and recorded higher seed yield of 1088 kg ha−1. Additionally, this method generated 8.1% higher net income and 12.5% more benefit–cost ratio than hand weeding, making it an economically profitable strategy for maximizing blackgram yield and effective labour management. [ABSTRACT FROM AUTHOR]

Rice cultivation in Tamil Nadu, a southern state of India, is a vital component of its agricultural landscape and economy. It holds historical significance and contributes significantly to the state's food security. Green manuring–system of rice intensification (SRI)–blackgram (rice fallow pulses) cropping system is novel, and this integrated system appears to be holistic and sustainable approach, combining innovative farming techniques to optimize yields, improve soil health, and minimize environmental impacts. To evaluate this, field demonstrations were conducted at a farmer's field through the National Pulses Research Centre, Vamban, Pudukkottai, Tamil Nadu, within the kharifrabi and summer seasons of 2019–20 under the Tamil Nadu Irrigated Agriculture Modernization project. The experimental site was medium deep clay with soil pH of 8.51, EC of 0.26 d S m−1, low in available nitrogen (212.02 kg ha−1), high in P2O5 (23.24 kg ha−1), and medium in K2O (300.46 kg ha−1). Initially, farmers were given the awareness about the improved production technologies (IPT), and then, demonstration was conducted in 50 hectares with 92 locations of Ponnaniyar sub-basin. The demonstration results showed that the improved practice of SRI recorded higher plant height and other yield attributes. Notably, the SRI cultivation method exhibited a range of yields from 7580 to 9400 kg ha−1 of rice across various locations, with the highest recorded at Avoor village. Concurrently, within the IPT framework for the GM–SRI–Rice fallow pulses cropping system, the recorded yields for Rice fallow Blackgram ranged from 590 to 730 kg ha−1. Comparative analysis indicated a remarkable 39.9 percent enhancement in system productivity through the adoption of IPT practices compared to conventional farmer practices. Moreover, the IPT framework showcased significantly higher water productivity, recording 0.7087 kg ha−1 m−3 compared to the conventional method, which yielded 0.2512 kg ha−1 m−3. Soil nutrient observations highlighted that these cropping systems positively impacted soil fertility parameters, compared to the initial available nutrient status. This augmentation in soil fertility could be attributed to the incorporation of green manures. Consequently, the green manure–system of rice intensification–rice fallow pulses crop sequences emerged as more productive and sustainable option, displaying the potential to enhance soil productivity and fertility status compared to conventional rice–blackgram/groundnut cropping sequences. These systems present promising alternatives for farmers within the Ponnaniyar sub-basin area of Tamil Nadu. [ABSTRACT FROM AUTHOR]

The present investigation was carried out with 53 genotypes of cowpea to study association among yield contributing traits. The genotypes were sown in a randomized block design replicated with three replications at the National Pulse Research Centre, TNAU, Vamban, Pudukkottai, during Kharif, 2022. Observations on ten traits were recorded and subjected to statistical analysis. The coefficient of phenotypic differences was substantially greater than the genotypic variation for all the characters under study, showing that the prevailing variation is mainly governed by the genotypic factor and substantial environmental factors. High heritability and genetic advance as percentage of mean were observed for the traits seed yield and 100 seed weight. This showed the role of additive gene action in the inheritance of these traits which gives the best chance of their improvement through simple selection. Association analysis revealed that number of cluster/plant and number of pods/plant had a significant and positive correlation on seed yield. Yield component traits viz., plant height, number of branches/plant, number of pods/cluster, number of pods/plant, 100 seed weight and seed yield were high inter-correlated among themselves. Furthermore, it was observed that the number of pods/plant, pod length, and 100 seed weight had the maximum positive direct effects on seed yield. So direct selection for these three traits could help to improve the seed yield potential of cowpea. [ABSTRACT FROM AUTHOR]

Globally renewable energy sources play a vital role in future energy production. Heat transfer and generation of drag are the two crucial elements in the global energy consumption chain. In this review analysis, we have emphasized how to improve these two areas to extract most of the energy available in nature and to enhance convective internal heat transfer for effective cooling of applications in automobiles, aerospace, electronics, heating, and cooling devices. Heat transfer enhancement achieved in many ways and using riblets is one of the methods of implementing passive techniques. Riblet applications in wind turbines employed with specially created microstructured surfaces and provide a variety of benefits which include a reduction in drag over a wind turbine especially operating at low Reynolds number. The overall drag reduction over a wind turbine depends on height, shape and whether it is fully or partially applied on aerofoil surfaces. Earlier studies proved that optimum drag reduction was achieved by employing riblets with varied geometry along the aerofoil. This analysis discusses the influence of riblets used in wind, hydro and gas turbines, which follows a comparison of heat transfer and aerodynamic effects. [ABSTRACT FROM AUTHOR]

The present study aims to evaluate the genetic variability, character association and path coefficient for various quantitative traits in fifty rice genotypes comprising of landraces and varieties under water limited conditions. Drought being a major abiotic stress causes severe reduction in grain yield especially during reproductive stages. The results revealed the presence of high genetic variability and significance for all the traits studied. The traits like single plant yield, number of filled spikelets per panicle, number of productive tillers per plant, flag leaf length, leaf rolling and leaf drying showed high variability compared to all other traits. The traits viz., spikelet fertility, number of productive tillers per plant, number of total tillers per plant, plant height, flag leaf breadth, panicle length, number of filled spikelets per panicle and relative water content exhibited a significant and positive correlation and direct effect on single plant yield, suggesting the potential importance of these traits in the improvement and selection of stable and high-yielding rice genotypes. [ABSTRACT FROM AUTHOR]

In this work expired drug namely, labetalol has been used as a corrosion inhibitor in a stirred acidic medium (1M HCl) with mass loss measurement and electro analytical techniques. . The efficiency of the inhibitor increases as the temperature increases from 30-60°C. The results reveal that maximum inhibition efficiency is observed as 91.66% for 500 ppm. Potentiodynamic polarization measurement implies that it perform as a mixed type inhibitor. FT-IR analysis prove that corrosion product is formed on the mild steel surface. When compared with metal in IM HCl, it includes a shielding layer on the surface of mild steel by controlling further attack of acid. SEM and SFM surface morphology study have also been performed to confirm the effect of labetalol on metal. In addition to that, DFT is also carried out to analyse HOMO, LUMO, ∆E and Mulliken charges. [ABSTRACT FROM AUTHOR]

A high yielding, input responsive, non lodging varagu culture TNPsc 176 was developed at the Centre of Excellence in Millets, Athiyandal. It is a pure line selection from DPS 63. It is characterized by tufty and medium tall plant stature, strong and sturdy culm with regular, long panicles. It has 10-15 productive tillers/plant and non-shattering grains on the panicle. It matures in 105-110 days and has recorded 2506 and 4394 kg/ha of grain and straw yield, respectively in a total of 223 trials under rainfed conditions during the past eight years of evaluation at national and state levels. The yield was observed to be 12.5 and 13.8 per cent increase in terms of grain and 15.5 and 13.0 per cent increase in terms of straw over the check varieties, CO3 and TNAU 86, respectively. In view of the superior performance and desirable attributes viz., easy threshability, synchronized maturity, non-lodging growth habit, bold and attractive light brown-coloured grains with good nutritional and cooking qualities, the varagu culture TNPsc 176 was released as ATL 1 during the year 2021, for cultivation in Ariyalur, Cuddalore, Dharmapuri, Madurai, Perambalur, Pudukottai, Salem, Vellore, Villupuram and Virudhunagar districts of Tamil Nadu. [ABSTRACT FROM AUTHOR]

The article titled "Retraction Note: EoT-driven hybrid ambient assisted living framework with naïve Bayes–firefly algorithm" has been retracted by the Editor-in-Chief and the publisher due to concerns regarding compromised editorial handling and peer review process, inappropriate references, and not being in scope of the journal or guest-edited issue. The authors have not responded to correspondence regarding this retraction, and the publisher has been unable to obtain a current email address for one of the authors. Springer Nature, the publisher, remains neutral in regards to jurisdictional claims and institutional affiliations. [Extracted from the article]

This document is a retraction note for an article titled "A new binary salp swarm algorithm: development and application for optimization tasks" published in the journal Neural Computing & Applications. The article has been retracted by the Editor-in-Chief and the publisher due to concerns regarding compromised editorial handling and peer review process, inappropriate or irrelevant references, and not being in scope of the journal or guest-edited issue. One of the authors disagrees with the retraction, while two authors have not responded to correspondence. The publisher, Springer Nature, remains neutral in regards to jurisdictional claims and institutional affiliations. [Extracted from the article]

This document is a retraction note for an article titled "A novel method for solving the fully neutrosophic linear programming problems" published in the journal Neural Computing & Applications. The article has been retracted due to concerns regarding compromised editorial handling and peer review process, inappropriate references, and being out of scope for the journal. The authors have not responded to correspondence regarding the retraction. Springer Nature, the publisher, remains neutral in regards to jurisdictional claims and institutional affiliations. [Extracted from the article]

The normal methods for monitoring environmental pollution with image data have many false positives. Therefore, this study is proposing a single-valued neutrosophic set (SVNS) (a variant of NS)-based method as a decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators for feature extraction and automatic material classification in mining area using satellite data. The experimental results show that this decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators for feature extraction and automatic material classification can better predict the presence of four heavy metals, i.e., vanadium(V), iron (Fe), copper (Cu), and nickel (Ni) in the study area than other methods. For vanadium metal, the determination accuracies, namely, producer accuracy, user accuracy, overall accuracy, and Kappa were 94.5%, 94.1%, 93.88%, and 0.93%, respectively. It was found that the estimated results and the distribution trend of heavy metals are almost the same as in actual ground measurements. [ABSTRACT FROM AUTHOR]

The cotton variety CO 18 (culture TCB 37) is a Gossypium barbadense variety identified for release by Tamil Nadu Agricultural University, Coimbatore during 2020. This variety was developed at the Department of Cotton, through selection from EC 101786. This variety recorded a mean seed cotton yield of 872 kg/ha as against 621 kg/ha of the Zonal check variety Suvin under irrigated condition. The percentage increase in seed cotton yield was 40.4 per cent. The variety has a yield potential of 1720 kg/ha as has been recorded in Rahuri centre during 2017-18. The variety has recorded an Upper Half Mean Length (UHML) of 35.1 mm, micronaire of 3.5 µg/inch and bundle strength of 36.2 g/tex in HVI mode. The variety recorded a mean boll weight of 3.1 g/boll, is superior to check Suvin and qualifying varieties (GSB 41 and ARBB 1302). CO18 combines high yield potential, big boll size and basic tolerance to pests and diseases. The culture TCB 37 was identified for release as CO 18 by Central Variety Identification Committee during 2020 and the farmers will be highly benefitted by cultivating this variety. [ABSTRACT FROM AUTHOR]

In agriculture, fungi can cause serious damage, resulting in critical losses of yield, quality and profit. Fungicides help in reducing the damage caused by fungus, reduce the yield loss and play a major role in quality improvement. The present investigation was carried out at Tamil Nadu Agricultural University to evaluate the influence of trifloxystrobin 50% + tebuconazole 25% (Nativo 75WG) on the yield and quality improvement on Bunny hybrid Bt cotton. Nativo 75WG was applied on the leaves of cotton plants at 40-60 (DAS) and 60-80 (DAS) at the concentration of 250, 300, 350 g/ha and Carbendazim @ 500 g/ha. The observations recorded were related to yield and quality attributes in all treatments. The application of Nativo @ 300 g/ha showed a significant increase in boll weight (4.86 g), lint yield per boll (3.86 g boll-1) and lint per plant (138.48 g plant -1) than other treatments. With respect to seed cotton yield and harvest index (0.37 %), the Nativo @ 300 g/ha registered a higher yield (20.2 %) and HI than control under the irrigated situation. Foliar application of treatments during the flowering stage (40-60 DAS) and boll formation stages (60-80 DAS) had increased the quality parameters such as fiber length (2.5% staple length, 50 % staple length) and fiber strength. Further, the foliar spray of Nativo @ 300 g/ha applied to bunny hybrid Bt cotton had resulted in a higher yield (2920.15 kg ha-1) due to an increase in leaf area index, greenness of leaf and higher dry matter production of the plant. [ABSTRACT FROM AUTHOR]

E‐learning is the indispensable technique to educate huge number of people and students in short period of time with optimized usage of different kind of required resources. It is employed as a crucial teaching approach by almost all kind of educational institutions all around the world. Since e‐learning involves significant amount of resource utilization and cost, it requires some essential methodology to enhance the current system of e‐learning more efficient. The mere publication of the educational content in websites is not enough. It is very clear that, without applying suitable strategic models and concepts and establishing appropriate communication channels between contributors of e‐learning system, the educational goals cannot be achieved as we desired. Distributed database involves greater contribution in the field of cloud based e‐learning process. Basically, data replication is crucial decision of companies as database distribution can be achieved effectively by the method of database replication which generates the same copies of information called replicas. In this article, we analyze the supremacy of synergetic learning and concentrates on data replication's significance in cloud based learning system. Here we propose an excellent mechanism for data replication and enhancing the performance in terms optimized access and update of data by the determination of exact location of data through dynamic programming. The efficiency of proposed mechanism is clearly illustrated by experimental results. [ABSTRACT FROM AUTHOR]

The localization strategy is broadly utilized in Wireless Sensor Networks (WSNs) to detect the present location of the sensor nodes. A WSN comprises of multiple sensor nodes, which makes the employment of GPS on each sensor node costly, and GPS does not give accurate localization outcomes in an indoor environment. The process of configuring location reference on each sensor node manually is also not feasible in the case of a large dense network. Hence, this proposal plans to develop an intelligent model for developing localization pattern in WSN with a group of anchor nodes, rest nodes, and target nodes. The initial step of the proposed node localization model is the selection of the optimal location of anchor nodes towards the target nodes using the hybrid optimization algorithm by concerning the constraints like the distance between the nodes. The second step is to optimally determine the location of the rest node by reference to the anchor nodes using the same hybrid optimization algorithm. Here, the weight has to be determined for each anchor sensor node based on its Received Signal Strength (RSS), and RSS threshold value with the assistance of Neural Network. The hybrid optimization algorithms check the direction to where the concerned node has to be moved by merging the beneficial concepts of two renowned optimization algorithms named as Rider Optimization Algorithm (ROA), and Chicken Swarm Optimization Algorithm (CSO) to solve the localization problem in WSN. The newly developed hybrid algorithm is termed as Rooster Updated Attacker-based ROA (RUA-ROA). Finally, the comparative evaluation indicates a significant improvement in the proposed localization model by evaluating the convergence and statistical analysis. [ABSTRACT FROM AUTHOR]

In this study, a cost and time saving strategy for the recovery of biomethane from rice straw using a novel phase-separation delignification process prior to bacterial pretreatment (BP) has been reported. The rice straw was subjected to delignification using mechanical homogenization to remove its lignin content, which enhanced the mass transfer of cellulose to the second step, the cellulase secreting bacterial pretreatment (CBP) process. The results showed that a higher lignin removal efficiency of 72% was achieved at an optimal biomass to water ratio of 0.04 (w/v) and a specific energy input of 114.3 kJ kg−1 TS. With a pretreatment time of 24 h, the delignified rice straw with the cellulase secreting Bacillus sp. pretreatment process resulted in the formation of higher fractions of soluble organics (38.2%), cellulose (35.2%), and hemicellulose (31.8%) than CBP (22.92%, 21.2%, and 19.1%) and the control (3.43%, 3.16%, and 2.9%), respectively. The D-CBP (delignified cellulase secreting bacterially pretreated) rice straw samples achieved a maximum lignin content of 0.5 g L−1 that did not inhibit the methanogenesis process, resulting in a specific biomethane production of 165 mL g−1 VS. The results from large scale energy balance analysis revealed that D-CBP saved a maximum energy of 769.08 kW h ton−1. The results from economic analysis for D-CBP indicated a net profit of 134.89 USD per ton and a cost–benefit ratio of 1.52 which were better than CBP (−86.07 USD per ton and 0.71). [ABSTRACT FROM AUTHOR]

The cotton compact variety CO 17 (culture TCH 1819) is a short duration genotype (125-130 days) with synchronized boll maturity suitable for high density planting system (HDPS) was released by Tamil Nadu Agricultural University, Coimbatore during 2020. This variety was developed at Department of Cotton, TNAU, Coimbatore through hybridization involving Khandwa 2 and LH 2220 followed by pedigree breeding. The variety possesses zero monopodia with short sympodial length and is highly suited for high density planting system. It recorded an average seed cotton yield of 2361 kg/ha which is 18.9% increase over the check variety Suraj (National check entry identified for HDPS). Culture TCH 1819 recorded seed cotton yield of 3427 kg/ha which was 21.7 % increase over Suraj and 29.0 % increase over MCU 7 under rice fallow condition. It recorded seed cotton yield of 2051 kg/ha which was 13.8 % increase over Suraj under summer irrigated conditions and also recorded 1604 kg/ha of seed cotton yield under winter rainfed which was 20.1 % increase over the check Suraj. This culture was also evaluated in All India Coordinated Cotton Improvement Project trials for two years during 2016-17 and 2017-18 across ten locations. It registered seed cotton yield of 1850 kg/ha which was 37.9 % increase over Suraj.TCH 1819 culture recorded Upper Half Mean Length (UHML) of 27 mm with bundle strength of 26.9 g/tex. It can spin upto 40's counts. [ABSTRACT FROM AUTHOR]

The research work for studying genetic variability, heritability and genetic advance as well as correlation and path coefficient analysis for 13 traits with sixty seven genotypes of upland cotton was carried out during winter 2017 at Cotton Research Station, Srivilliputtur, Tamil Nadu Agricultural University, India. The variability studies indicated that the high estimate of phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) was observed for seed cotton yield per plant. High phenotypic coefficient of variation and moderate genotypic coefficient of variation was observed for the number of monopodia per plant. High heritability observed for the number of monopodia per plant, the number of bolls per plant, seed index, lint index, ginning percentage, 2.5% span length, uniformity ratio, bundle strength, fibre fineness and seed cotton yield per plant. The number of monopodia per plant, the number of bolls per plant, bundle strength, fibre fineness and seed cotton yield per plant shows high heritability coupled with high genetic advance over mean indicating the preponderance of additive gene action in the inheritance of these traits. The correlation study revealed that plant height, the number of monopodia per plant, the number of sympodia per plant and the number of bolls per plant had a significant positive association with seed cotton yield per plant. Path analysis indicated that the number of bolls per plant and seed index have maximum positive direct effect on seed cotton yield per plant. [ABSTRACT FROM AUTHOR]

Recently, neutrosophic systems modeling gained great attention in indeterminacy handling. Generating suitable membership, indeterminacy and non-membership functions for neutrosophic temperature variable in the forest fires data is a challenging problem. This paper presents a platform for integrating information theory measures with particle swarm optimization as a meta-heuristic algorithm to model a neutrosophic variable out of concrete data. Particle swarm optimization is an efficient population-based algorithm presented to find membership, indeterminacy and non-membership optimal partitions. The integrated hybrid model of information theory measures and particle swarm optimization is presented. The experimental comparisons are applied between the proposed technique and the Fuzzy C-Mean MATLAB tool for generating fuzzy membership functions. Extensive graphical results of the temperature variable of the forest fires data set and the conversion rates are provided to demonstrate the effectiveness of the proposed paradigm. The conversion rates of the proposed technique show that the optimal membership and non-membership functions are concluded after 10 and 17 iterations, respectively, which is a feasibly fast rate. Also, the proposed technique generated relatively similar functions' subsets for the forest fire temperature variable, while the Fuzzy C-Mean clearly shifted the functions' subsets during the experiments. Calculating the indeterminacy of the temperature in forest fire data will contribute to forecasting these fires accurately. [ABSTRACT FROM AUTHOR]

The design of Two-Dimensional Infinite Input Response Filters (2D IIR) is an important task in the field of signal processing. These filters are widely used in several areas of engineering as an important tool to eliminate undesired frequencies in high-noised signals. However, 2D IIR filters have parameters that need to be calibrated in order to obtain the best output, and finding these optimal values is not an easy task. On the other hand, Electro-magnetism Optimization (EMO) is a population-based technique which possess interesting convergence properties, it works following the electro-magnetism principles for solving complex optimization problems. This paper introduces an algorithm for the automatic parameter identification of 2D IIR filters using EMO, a process that is regarded as a multidimensional optimization problem. Experimental results are included to validate the efficiency of the proposed technique regarding accuracy, speed, and robustness. [ABSTRACT FROM AUTHOR]

Fifty intra hirsutum hybrids developed by involving five female and ten testers were evaluated to study gene action, combining ability and heterosis. The analysis of variance indicated substantial variability among the experimental material for yield, its contributing components and fibre quality traits. Preponderance of non-additive gene action was obtained for seed cotton yield per plant, majority of its component traits and fibre quality traits. Three parents namely TSH 321, BGDS 1055 and GSHV 177 were identified as very good general combiners for yield and fibre quality traits. The cross combinations TSH 321 x African I-2, and CCH 15-1 x GSHV 177 for seed cotton yield per plant and fibre properties whereas the crosses BGDS 1055 x GSHV 177, TSH 321 x TCH 484-4, SHM 55 x TCH 1819 for seed cotton yield per plant and number of bolls per plant were identified as the best hybrids and these are well suited for exploitation through heterosis breeding as it recorded significant per se, sca effects and standard heterosis for trait as mentioned. Further, the crosses namely SHM 55 x CNH 19, SHM 55 x GSHV 177 and SHM 55 x TCH 486-7 could be recommended for recombination breeding as they satisfied the criteria of the presence of significant gca effects of the parents and non significant sca effects for yield and fibre quality traits. The study also revealed good scope for commercial hybrid development as well as isolation of pure lines among the progenies. [ABSTRACT FROM AUTHOR]

The investigation was carried out to estimate combining ability, heterosis and hybrids suitable for recombination breeding for seed cotton yield, yield components and fibre quality traits designed among 13 parents and 40 hybrids developed through line x tester method. The analysis of variance indicated substantial variability among the experimental material for yield, its components and fibre quality traits. All the characters were predominantly controlled by non-additive gene action. Four parents namely TCH 1819, TSH 0499, SVPR 2 and SVPR 5 were identified as very good general combiners for most of the yield and fibre quality traits. The cross combinations, TCH 1819 x TSH 0499, TCH 1819 x MCU 7, KC 2 x SVPR 4, TCH 1818 x SVPR 5, TCH 1818 x SVPR 2 and TCH 1818 x CO 14 were identified as the best hybrids and these are well suited for exploitation through heterosis breeding as it recorded highest per se performance, significant sca effects and standard heterosis for important yield components and fibre quality traits. Besides, the crosses viz., TCH 1819 x SVPR 5 and TCH 1819 x SVPR 4 could be recommended for recombination breeding as they satisfied the presence of significant gca effects of the parents and non significant sca effects. [ABSTRACT FROM AUTHOR]

In our rapidly-growing big-data area, often the big sensory data from Internet of Things (IoT) cannot be sent directly to the far data-center in an efficient way because of the limitation in the network infrastructure. Fog computing, which has increasingly gained popularity for real-time applications, offers the utilization of local mini data-centers near the sensors to release the burden from the main data-center, and to exploit the full potential of cloud-based IoT. In this paper, a high-performance approach based on the Max–Min Ant System (MMAS), which is an efficient variation in the family of ant colony optimization algorithms, is proposed to tackle the static task-graph scheduling in homogeneous multiprocessor environments, the predominant technology used as mini-servers in fog computing. The main duty of the proposed approach is to properly manipulate the priority values of tasks so that the most optimal task-order can be achieved. Leveraging background knowledge of the problem, as heuristic values, has made the proposed approach very robust and efficient. Different random task-graphs with different shape parameters have been utilized to evaluate the proposed approach, and the results show its efficiency and superiority versus traditional counterparts from the performance perspective. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is one of the recent meta-heuristic algorithms that imitate the behaviors of salps during the navigating and foraging in oceans to perform global optimization. However, the original study of this algorithm was proposed to solve continuous problems, and it cannot be applied to binary problems directly. In this paper, a new binary version of the SSA named BSSA is proposed based on a modified Arctan transformation. This modification has two features regarding the transfer function, namely multiplicity and mobility. By this modification, the exploration and exploitation capabilities can be enhanced. The proposed BSSA is compared among four variants of transfer functions for solving global optimization problems. Also, a comparative study with different binary algorithms including binary particle swarm, binary bat algorithm and binary sine–cosine algorithm on twenty-four benchmark problems is conducted. Furthermore, the nonparametric statistical test based on Wilcoxon's rank-sum is carried out at 5% significance level to judge statistically the significant of the obtained results among the different algorithms. The results affirm the superior performance of the modified BSSA variant over the other variants as well as the existing approaches regarding solution quality. [ABSTRACT FROM AUTHOR]

In the current decade, ambient assisted living is attracting widespread interest due to the rapidly aging global population. The cloud-based Internet of things (IoT) healthcare systems are facing many barriers to handle the big healthcare data that IoT generates. Edge of things computing is one of the promising solutions. Accordingly, this paper proposes a hybrid ambient assisted living framework with naïve Bayes–firefly algorithm (HAAL-NBFA) for monitoring elderly patients suffering from chronic diseases. This architecture exploits the current advances in the IoT by using ambient and biomedical sensors to collect the data of the elderly patient and then fuse it into context states to predict the health status of the patient in real time using context-awareness techniques. The proposed HAAL-NBFA framework proposes a five-phase classification technique to handle big imbalanced datasets resulting from long-term monitoring of elderly patients. In this paper, the firefly algorithm (FA) has been used to optimize naïve Bayes classifier (NB) which selects the minimum features that give the highest accuracy. The proposed NB-FA acts as a safe-fail module that decides when to stop the system and when to permit its continuation in case of sensor's failure. The experimental results proved that the proposed HAAL-NBFA had achieved high accuracy and sensitivity in predicting the health status of patients suffering from blood pressure (BP) disorders. Furthermore, the importance of NB-FA in accelerating classifications and maintaining the continuity of HAAL-NBFA's operation has been proved by experimental results. [ABSTRACT FROM AUTHOR]

The most widely used technique for solving and optimizing a real-life problem is linear programming (LP), due to its simplicity and efficiency. However, in order to handle the impreciseness in the data, the neutrosophic set theory plays a vital role which makes a simulation of the decision-making process of humans by considering all aspects of decision (i.e., agree, not sure and disagree). By keeping the advantages of it, in the present work, we have introduced the neutrosophic LP models where their parameters are represented with a trapezoidal neutrosophic numbers and presented a technique for solving them. The presented approach has been illustrated with some numerical examples and shows their superiority with the state of the art by comparison. Finally, we conclude that proposed approach is simpler, efficient and capable of solving the LP models as compared to other methods. [ABSTRACT FROM AUTHOR]

In big data, the information retrieval (IR) embraces the discovery of documents from a compilation of dataset which are related to the user query. Usually, the data retrieval systems are used to scan the data. The existent approaches that employ customary IR are wearisome for big document collections. Recently, IR approaches were developed, albeit these are faster comparing to the traditional method but the quality of the document retrieved is less. To overcome such difficulties, here, effectual big data retrieval utilizing Deep Learning Modified Neural Networks (DLMNN) is proposed. Initially, the general pre-processing along with feature extraction steps are taken place. In preprocessing stage, unwanted data are removed and also used for changing the unstructured data in to structured data then in FE is done using frequency and entropy calculation of the given input data. Secondly, find the closed recurrent item dataset, after that find the weight of provided data using entropy measure and frequent item measure. In the 3rd step, the documents are clustered utilizing the k-means algorithm and then classified using DLMNN. The K-Means algorithm is utilized to partition the collection of documents into several clusters then the DLMNN is used for classifying the documents into positive and negative classes. The proposed DLNN weight parameters are optimized utilizing the cuckoo search (CS) optimization algorithm. The last stage on the training process section is generating a training data-base. In the other part, the retrieval process is performed, in this section it pre-processes the user query and discovers the frequency item set then gets retrieval data. Finally, check the similarity assessment, if the information is found then it is visualized, otherwise the document is returned to the initial position. Experimental results contrasted with the previous MRT and IRI-RAS techniques concerning precision, recall, F-measure along with computation time. The proposed document IR is better when comparing with existent methods. [ABSTRACT FROM AUTHOR]

Big data is centered upon the technique of expanding volume of high velocity, intricate along with different kind of data. Organizations that hold vast sum of data deals with new creation of systematic tools intended for large data. The conventional data-intensive business application starts to go down behind the times, on account of the deficient abilities to manage large data volumes, unstructured information, low rate of information retrieval along with complex algorithms. Big data relies upon the data complexity, relatively than the data size only. For resolving this kind of trouble, this paper establishes a mutual refinement technique for big data retrieval to augment the performance. The intended system comprises the system of training and retrieval which is performed consecutively. In training process, initially input data is preprocessed by splitting the data. Then frequency and entropy features are extracted from the preprocessed data. After the feature extraction data is exhibited to the mutual refinement process. In mutual refinement step hash tag graph is generated to train the data and this removes the uncertainty from the data. In retrieval process, the input query data is used for the similarity assessment. Features like frequency and entropy are extracted from the query data. Then the feature value is compared with the hash tag graph. If the feature value is matched then the data is retrieved as of the hash tag graph and the retrieved data is visualized. The proposed technique's performance is assessed by relating our intended work with the other conventional works. The experimental output exhibits that our intended mutual refinement process augments the system performance process by confiscating the uncertainty comprised in the system. This work offered a unique mutual refinement approach which yields better outcomes for retrieving the big data in a proficient manner. The proposed process retrieving process in big data gives the better performance but in future, experiments can be done on large datasets and some real-time applications to calculate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A high yielding long staple hirsutum cotton variety SVPR 6 (TSH 04/115) was developed by Cotton Research Station, Srivilliputtur and released in the year of 2017. It was notified during the year 2018 for kharif season for the southern zone of India under winter irrigated and summer irrigated tracts of Tamil Nadu. It is a hybrid derivative of the cross between SVPR 2 and BJA 592. SVPR 6 cotton is semi spreading in nature with determinant growth habit and matures in 155 days. It has recorded its potential yield of 3392 kg/ha at Raichur during 2013-14. The overall performance revealed that the cotton culture TSH 04/115 was superior over Suraj and SVPR 4 with an average seed cotton yield of 2285 kg/ha which is 11.5 % and 30.9 % increase over the zonal check Suraj (2049 kg/ha) and local check SVPR 4 (1589 kg/ha) respectively . This culture comes under the long staple category with an average upper mean half length of 29.5 mm, fibre strength of 27.3 g/tex and micronaire value of 4.0. It can spin upto 40's counts. It is moderately resistant to jassid and white fly besides tolerance to Bacterial leaf blight and Alternaria leaf spot. [ABSTRACT FROM AUTHOR]

The present experiment was carried out at research farm of Cotton Research Station, Srivilliputtur, to determine genetic variability, heritability and genetic advance estimates in upland cotton. The analysis of variance involving a set of fourty two improved hirsutum genotypes received from various research institutes under AICRP on Cotton for eleven characters viz., Days to 50% flowering, Plant height, Number of monopodia per plant, Number of sympodia per plant, Number of bolls per plant, Boll weight, Ginning percentage, Upper Half Mean Length, Bundle strength, Micronaire value, and Seed cotton yield (kg/ha) revealed highly significant mean sum of squares for all the characters indicating greater diversity among the genotypes. The value of Phenotypic Coefficient of Variation (PCV) is greater than Genotypic Coefficient of Variation (GCV); it means that the apparent variation is not only due to genotypes but also due to influence of environment. The difference between the estimates of PCV and GCV were low for all the characters except number of monopodia per plant, number of sympodia per plant and number of bolls per plant thereby indicating that lesser role played by the environment in the expression of these characters. The traits like Days to 50% flowering, Plant height, Boll weight, Seed cotton yield (kg/ha), Upper half mean length, Micronaire value and Bundle strength exhibited high broad sense of heritability and moderate sense of heritability was observed for Number of monopodia per plant, Number of sympodia per plant, Number of bolls per plant and Ginning percentage. High heritability coupled with high genetic advance as a percent of mean was recorded for boll weight, seed cotton yield and micronaire value indicated the major role of additive gene action in the inheritance of these characters. Thus, these characters may serve as an effective selection parameter during breeding programme in the upland cotton. [ABSTRACT FROM AUTHOR]

Cotton Hybrid SVPR 1 is a promising long staple intra hirsutum hybrid developed at Cotton Research Station, Srivilliputtur. It was identified by the Central Varietal Identification Committee in the year of 2015 and notified in 2016 for kharif season of southern zone of India for irrigated condition and winter/summer irrigated tracts of Tamil Nadu. It is a cross between cultures TSH 311 and TSH 306. Cotton Hybrid SVPR 1 is open type in architecture with determinant growth habit and maturing in 165 days. It has recorded a mean seed cotton yield of 2216 kg/ha which is 16.4% increase over the check Bunny (1904 kg/ha). This hybrid comes under the long staple category with 2.5% span length of 28.5 mm, fibre strength of 21.9 g/tex and micronaire value of 4.0. It can spin to 40's counts. It is moderately resistant to leaf hopper, Bacterial Leaf Blight and Alternaria Leaf Spot. Hence, the cotton hybrid SVPR 1 (TSHH 0629) was released for the winter/summer irrigated areas of Tamil Nadu and Kharif season of South Zone of India. [ABSTRACT FROM AUTHOR]

SVPR 5 is a high yielding long staple hirsutum cotton variety developed by Cotton Research Station, Srivilliputtur. It was released in the year of 2014 and notified during the year 2016 for kharif season of southern zone of India under for winter and summer irrigated tracts of Tamil Nadu. It is a hybrid derivative of the cross between NDLH 1658 and Surabhi. SVPR 5 cotton is semi spreading in nature with determinant growth habit and matures in 150 days. It has recorded a mean seed cotton yield of 1845 kg/ha which is 13.0% increase over the zonal check Surabhi in AICCIP trials. The overall performance revealed that the cotton culture TSH 0250 was superior over SVPR 2 and SVPR 4 with an average seed cotton yield of 1661 kg/ha against 1583 kg/ha of SVPR 2 and 1425 kg/ha of SVPR 4. Its fibre qualities are comparable to Surabhi with 29.4 mm span length and 22.0g/tex of bundle strength. The cotton SVPR 5 can able to spin to 50s counts which suit the current demand of the textile industries. It is moderately resistant to leaf hopper, bacterial leaf blight, Alternaria leaf spot and grey mildew. [ABSTRACT FROM AUTHOR]

A novel semiorganic non-linear optical crystal,l-lysine hydrochlorobromide (LYHCBr) dimensions of 1 × 0.2 × 0.2 cm was grown by deionised water by the conventional method.l-lysine hydrochlorobromide crystallises in non-centrosymmetric orthorhombic crystal system with space group P212121.FTIR spectrum analysis was examined to assign the functional groups of the grown crystal. The optical study shows that the crystal has lower UV cutoff wavelength at 208 nm. Thermal analysis confirms that the crystal is stable up to 244.8°C. The dielectric constant and dielectric loss of the grown crystal were determined at various temperatures in the frequency range of 100 Hz to 1 MHz. The mechanical property of the grown crystals was analysed through Vickers microhardness study. The positive photoconducting nature of the sample was verified by photoconductivity study. The Kurtz–Perry second harmonic generation (SHG) study revealed that the efficiency of LYHCBr is 1.91 times higher than that of KDP. [ABSTRACT FROM AUTHOR]

A field study was conducted to investigate the impact of different concentration of Nativo 75 WG (trifloxystrobin 50% + tebuconazole 25%) on physiochemical changes in Bunny hybrid Bt cotton. In the present study, Nativo 75 WG was sprayed twice at different concentration of @ 250, 300, 350 g/ha along with Carbendazim @ 500 g/ha. The samples were collected 40, 60, 80, 120 days after sowing (DAS). While examining one of the physiological parameter i.e. chlorophyll index, Nativo @ 300 g/ha revealed significantly higher value. Similarly in biochemical parameters, such as soluble protein, nitrate reductase activity and accumulation of phenols, Nativo @ 300 g/ha showed positive influence over the stages. The application of fungicide Nativo 75 WG improves the growth of the plant in cotton during flowering and boll formation stage which ultimately resulted in greenness of leaf. Thus, Nativo @ 300 g/ha can be successfully employed as a foliar spray under field conditions to enhance the physiochemical changes in Bunny hybrid Bt cotton. [ABSTRACT FROM AUTHOR]

Indian cotton production has reached 375 lakh bales, after meeting 297 lakh bales of domestic demand besides sparing 90 lakh bales for export. However, the annual requirement of extra long staple (ELS) cotton is about 7.60 lakh bales against the current production of 5.0 lakh bales. In G. hirsutum species, the availability of ELS cotton varieties is very limited. The newly developed TCH 1716 is a high yielding genotype with a fibre length of 35.6mm. The boll size is also large. It is a boon in the production of extra long staple cotton in India. [ABSTRACT FROM AUTHOR]

In the present study, proton transfer from nitric and maleic acids to amine group (4-chloroaniline) led to hydrogen bonded crystals of 4-chloroanilinium maleate (4CAM) and 4-chloroanilinium nitrate (4CAN) which are investigated by the experimental and theoretical approaches. The molecular structures of these two compounds were optimized with the Density Functional Theory (DFT) using B3LYP function and the Hartree-Fock (HF) level with a6-311 ++G(d,p) basis set. Geometrical parameters of the molecules were also analyzed along with their intermolecular hydrogen bond, which tailors the ions. These analyses show that present molecules are stabilized through the N-H ⋯O and O-H ⋯O hydrogen bonds. The vibrational modes were computed by quantum chemical methods. Further, these modes are investigated by FT-IR and FT-Raman spectroscopy in the range of 4000-400 cm. The optimized molecular geometry and computed vibrational spectra are compared with experimental results, which show significant agreement. The natural bond orbital (NBO) analysis was carried out to interpret hyperconjucative interaction and intramolecular charge transfer (ICT). This analysis gives the precise insight into the nature of H-bond interactions. The chemical hardness, electronegativity and chemical potential of the molecules were determined by HOMO-LUMO plot. The frontier molecular orbitals have small band gap value, which signify the possible biological/pharmaceutical activity of the compounds. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]