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This research focuses on Wireless Sensor Networks (WSNs) and proposes a three-phase approach to achieve energy-efficient routing. The approach consists of node deployment using Voronoi diagrams, clustering, and Cluster Head (CH) selection using energy-efficient game theory, and a routing strategy based on Improved Pelican Optimization (ImPe) segment routing. Random deployment of sensor nodes in WSNs can lead to coverage issues, and hence, in order to address this, Voronoi-based node deployment is employed to ensure uniform and balanced coverage of the monitoring area. An energy-efficient game theory-based approach is used for CH selection by considering the energy levels to select CHs for enhancing network longevity. The proposed routing mechanism utilizes segment routing, which provides deterministic routing paths from CHs to the sink (Base Station). Segment routing eliminates the need for route discovery and maintenance, making it energy-efficient. The ImPe algorithm that works on the characteristics of pelican search agents is employed to choose the optimal segment path for information sharing. The assessment based on delay, network lifetime, packet delivery ratio, residual energy, throughput, communication overhead, and energy utilization acquired the values of 2.57, 98.59, 98.29, 0.98, 238.51, 7.71, and 0.02 respectively.

The use of an electrical energy storage system (EESS) in a microgrid (MG) is widely recognized as a feasible method for mitigating the unpredictability and stochastic nature of sustainable distributed generators and other intermittent energy sources. The battery energy storage (BES) system is the most effective of the several power storage methods available today. The unit commitment (UC) determines the number of dedicated dispatchable distributed generators, respective power, the amount of energy transferred to and absorbed from the microgrid, as well as the power and influence of EESSs, among other factors. The BES deterioration is considered in the UC conceptualization, and an enhanced mixed particle swarm optimizer (EMPSO) is suggested to solve UC in MGs with EESS. Compared to the traditional PSO, the acceleration constants in EMPSO are exponentially adapted, and the inertial weight in EMPSO decreases linearly during each iteration. The proposed EMPSO is a mixed integer optimization algorithm that can handle continuous, binary, and integer variables. A part of the decision variables in EMPSO is transformed into a binary variable by introducing the quadratic transfer function (TF). This paper also considers the uncertainties in renewable power generation, load demand, and electricity market prices. In addition, a case study with a multiobjective optimization function with MG operating cost and BES deterioration defines the additional UC problem discussed in this paper. The transformation of a single-objective model into a multiobjective optimization model is carried out using the weighted sum approach, and the impacts of different weights on the operating cost and lifespan of the BES are also analyzed. The performance of the EMPSO with quadratic TF (EMPSO-Q) is compared with EMPSO with V-shaped TF (EMPSO-V), EMPSO with S-shaped TF (EMPSO-S), and PSO with S-shaped TF (PSO-S). The performance of EMPSO-Q is 15%, 35%, and 45% better than EMPSO-V, EMPSO-S, and PSO-S, respectively. In addition, when uncertainties are considered, the operating cost falls from $8729.87 to $8986.98. Considering BES deterioration, the BES lifespan improves from 350 to 590, and the operating cost increases from $8729.87 to $8917.7. Therefore, the obtained results prove that the EMPSO-Q algorithm could effectively and efficiently handle the UC problem.

Many-objective optimization (MaO) deals with a large number of conflicting objectives in optimization problems to acquire a reliable set of appropriate non-dominated solutions near the true Pareto front, and for the same, a unique mechanism is essential. Numerous papers have reported multi-objective evolutionary algorithms to explain the absence of convergence and diversity variety in many-objective optimization problems. One of the most encouraging methodologies utilizes many reference points to segregate the solutions and guide the search procedure. The above-said methodology is integrated into the basic version of the Moth Flame Optimization (MFO) algorithm for the first time in this paper. The proposed Many-Objective Moth Flame Optimization (MaOMFO) utilizes a set of reference points progressively decided by the hunt procedure of the moth flame. It permits the calculation to combine with the Pareto front yet synchronize the decent variety of the Pareto front. MaOMFO is employed to solve a wide range of unconstrained and constrained benchmark functions and compared with other competitive algorithms, such as non-dominated sorting genetic algorithm, multi-objective evolutionary algorithm based on dominance and decomposition, and novel multi-objective particle swarm optimization using different performance metrics. The results demonstrate the superiority of the algorithm as a new many-objective algorithm for complex many-objective optimization problems.

Abstract Background In the protected cultivation of tomato (Solanum lycopersicum L.) crop, the severity of root-knot nematode, Meloidogyne incognita, incidence is alarming nowadays. To combat this, growers usually prefer using synthetic chemical pesticides, which in turn results in harming non-target beneficial microbes present in the soil micro-flora and indirectly toxic to human health. Therefore, attempts were made to find out the nematicidal potential of plant growth-promoting rhizobacteria (PGPR) against M. incognita, which could be used as an alternative solution to manage M. incognita incidence under protected cultivation. Results Nematicidal potential of three PGPR isolates and their consortium against M. incognita under laboratory, pots, and field experiments was studied. Juvenile mortality of 91.67% was recorded at 100% consortium, followed by 73.33–81.33% by individual isolates. Similarly, hatching inhibition of 84.26% was recorded at 100% PGPR consortium followed by 78.48–83.21% in individual isolates after 96 h. of incubation. In the pots’ study, soil drenched with PGPR isolates consortium, followed by Bacillus subtilis DTBS 5, Pantoea agglomerans, and Bacillus amyloliquefaciens DSBA 11 recorded significant reductions in the nematode incidence. Whereas in the field study, PGPR isolates applied as soil drenching also significantly reduced nematode’s incidence in consortium, followed by B. subtilis DTBS 5 and B. amyloliquefaciens DSBA 11-treated soil in both field experiments. On an average, the plant growth promotion and fruit yield were enhanced than untreated control and PGPR isolates applied as soil drenching gave a significant result than bare root dip treatment. Conclusions PGPR isolates, B. amyloliquefaciens DSBA 11, B. subtilis DTBS 5, and P. agglomerans, were found to be effective against M. incognita. This finding can be incorporated into the nematode management strategy in tomato crop grown under protected cultivation. Further to enhance the biocontrol efficacy of these PGPR isolates, suitable formulations of either individual or consortium need to be done.

The performance of a PhotoVoltaic (PV) system could be inferred from the features of its current–voltage relationships, but the PV model parameters are uncertain. Because of its multimodal, multivariable, and nonlinear properties, the PV model requires that its parameters be extracted with high accuracy and efficiency. Therefore, this paper proposes an enhanced version of the Gradient-Based Optimizer (GBO) to estimate the uncertain parameters of various PV models. The Criss-Cross (CC) algorithm and Nelder–Mead simplex (NMs) strategy are hybridized with the GBO to improve its performance. The CC algorithm maximizes the effectiveness of the population and avoids local optima trapping. The NMs strategy enhances the individual search capabilities during the local search and produces optimum convergence speed; therefore, the proposed algorithm is called a Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO). The primary objective of this study is to propose a simple and reliable optimization algorithm called CCNMGBO for the parameter estimation of PV models with five, seven, and nine unknown parameters. Firstly, the performance of CCNMGBO is validated on 10 benchmark numerical optimization problems, and secondly, applied to the parameter estimation of various PV models. The performance of the CCNMGBO is compared to several other state-of-the-art optimization algorithms. The results proved that the proposed algorithm is superior in handling the numerical optimization problem and obtaining the uncertain parameters of various PV models and performs better during different operating conditions. The convergence speed of the proposed CCNMGBO is also better than selected optimization algorithms with highly reliable output solutions. The average objective function value for case 1 is 9.83E−04, case 2 is 2.43E−04, and the average integral absolute error and relative error values are 1.05E−02 and 3.51E−03, respectively, for all case studies. With Friedman’s rank test values of 2.21 for numerical optimization and 1.66 for parameter estimation optimization, the CCNMGBO stood first among all selected algorithms.

The photovoltaic (PV) systems must work at the maximum power point (MPP) to derive the highest possible power with the higher performance during a change in operating conditions. The primary objective is to implement a novel hybrid tracking algorithm to extract the maximum output power from the solar PV panel or array under partial shading conditions (PSCs). This hybrid MPP tracking algorithm is based on the salp swarm algorithm (SSA), which finds the initial global peak (GP) operating point and is followed by the perturb and observation (P&O) algorithm in the last stage to realize a faster convergence rate. Thus, the computational burden met by the conventional methods such as standalone P&O, hybrid grey-wolf-optimization (HGWO), and hybrid whale-optimization algorithm (HWOA) algorithm reported in the literature is overcome by the proposed hybrid SSA algorithm called HSSA. The P&O algorithm searches the MPP in the projected search space by the SSA algorithm. The proposed hybrid algorithm is simulated using MATLAB/Simulink simulation tool to validate the effectiveness of tracking the MPP. The hybrid SSA is compared with the standalone P&O, hybrid WOA, and hybrid GWO, and from the simulation results, it is proved that the hybrid tracking algorithm exhibits a high tracking performance.

Healing of various ailments using herbal medicines is gaining much interest. Plants classified as grasses, specifically Cynodon dactylon, are an appreciated group of monocots used in many herbal remedies. In this work, C. dactylon, is grown naturally and also under market available LED Luminaires with different lighting conditions. Until 2010, most of the plants are grown under conventional lamps that are not spectrally tunable. Cynodon dactylon, the grass is grown under two different light spectrum, two light levels and three photoperiods (9hours, 12 hours, 15 hours) to extend our experiential knowledge. The biomass accumulation was the highest when grown under a lower RB ratio-12-hour- $163\mu $ mol/s, and phenolic content was the highest at 92.8 mg/g wt Gallic Acid Equivalents under combined light source at 15-hour photoperiod. A spectrally tunable LED light source with an optimal quantity of LEDs saves cost, space and energy. Considering the light parameters from the light sources used for growing C. dactylon, Levenberg–Marquardt (LM) algorithm is implemented to select an optimal LED quantity that composes the light source. The algorithm simulates the given target spectrum with minimum fitness error. The method applied to model LEDs, its validation against the practical LED spectrum, spectrum matching and computation of Luminous flux, Photosynthetic Photon Flux and efficacy are also presented. Many spectrums are simulated to validate the performance of the algorithm. A solution of optimal LEDs for three Photosynthetic Photon Flux (PPF) levels with LEDs is derived, and it is observed that the number of LEDs increased with PPF.

The photovoltaic (PV) systems should operate at a maximum power point (MPP) to extract the maximum possible output power with high tracking efficiency under various operating conditions This paper discusses a new maximum power point tracking (MPPT) technique to extract the peak power from the PV panel/array during partial shaded conditions (PSCs). The proposed algorithm is based on bio-inspired Whale Optimization (WO) with reinitialization process when the PV system is subjected to change in shading pattern, and the algorithm tries to locate the global peak (GP) with a high convergence rate and high tracking efficiency. The proposed algorithm eliminates the computational burden faced by the hybrid MPPT algorithms as discussed in various literature and reduces the power oscillation during the change in operating conditions. The proposed technique is modeled and simulated under different test conditions using MATLAB/Simulink software. The performance of the proposed technique is compared with conventional perturb and observation (PO), Grey Wolf Optimization (GWO) and hybrid GWO (HGWO) techniques in terms of tracking time and tracking efficiency and the simulation result proves that WO technique displays high tracking efficiency (>95%) and less convergence time (

This set of data is being used to calculate the design parameters of the solar photovoltaic (PV) charge controller. The data presented in this paper are used to design an advanced charge control with higher capacity and more possible real-time applications. This design parameters of the controller will be useful for the off-grid systems, automatic street light, and signs control, etc., which requires the medium-level power, and more reliable operation. The design set of parameters such as inductance, switching frequency, duty cycle, power loss, and the efficiency are given in this paper for the rating of the 500 W charge controller. The data captured are compared with the various power level selection along with the different power semiconductor switches.

Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.

In this study, a series of blended yarns consisting of 80:20 bamboo/cotton, 67:33 bamboo/cotton, 50:50 bamboo/cotton, 33:67 bamboo/cotton, 20:80 bamboo/cotton, 80:20 bamboo/polyester, 67:33 bamboo/polyester, 50:50 bamboo/polyester, 33:67 bamboo/polyester, and 20:80 bamboo/polyester were produced from blends consisting of bamboo/cotton and bamboo/polyester. Besides these, 100% bamboo, 100% cotton, and 100% polyester were also produced. All the yarns were produced with two levels of twist per meter (TPM) 76 and 90. It can be found that the yarn unevenness characteristic was affected by the blended ratio of cotton, polyester, and regenerated bamboo fiber. The drop in tenacity of blended yarns in comparison to the constituents is generally lower which is attributed to the elongation at break of the yarns. The variability in tenacity in respect of 100% polyester yarn is quite high in comparison with other yarns. The yarn elongation at break of bamboo/cotton-blended yarns is found to be lower than those of bamboo/polyester-blended yarns. The variability in the work of rupture is found to be lower for bamboo/cotton-blended yarns. Yarn friction values of the yarns noticed that polyester exhibits the highest value at the TPM of 76. The yarn torque values from which it is noticed that 100% polyester yarns spun with the TPM of 76 have a higher torque in both the wet and dry states. The quality characters of yarn depend upon the ratio of bamboo, cotton, and polyester in the blend ratio.

The conventional DC-DC converters such as SEPIC, boost converter, etc. produces large voltage ripples in multilevel converter systems. For that reason, in this paper, a new DC-DC converter topology is proposed, and the performance is analyzed. Since the proposed converter delivers high conversion efficiency, it can be selected for multilevel boost DC-DC converters. The adverse effects such as inductor resistance and inductor size of the conventional converters are overcome by the proposed converter. The output voltage ripples are reduced in the proposed converter, i.e., the spikes in the converter output voltage are almost zero. The theoretical analysis is presented in this paper, which speaks about the advantage of the proposed converter. The converter operation is analyzed and discussed in continuous conduction mode (CCM). The voltage gain of the proposed converter is higher than the conventional boost converter. To validate the performance of the proposed converter, an experimental prototype is fabricated and tested in the laboratory. The performance of the converter is compared with the conventional boost and SEPIC converter. The experimental result confirms the theoretical analysis. The proposed converter can be extended by connecting more number of voltage multiplier (VM) cells to get the desired multilevel output voltage.

Introduction: Malocclusion is one of the common public health problems worldwide. The study aimed to assess the self-perception of dental esthetics, malocclusion, and oral health-related quality of life (OHRQoL) and to determine the relation between them among 13–15-year-old schoolchildren in Bengaluru. Materials and Methods: In this cross-sectional study, 540 children were selected equally from government, aided, and private schools in Bengaluru. Self-perception of dental esthetics was measured using Oral Aesthetic Subjective Impact Scale (OASIS), whereas malocclusion and OHRQoL were assessed using Dental Aesthetic Index and Oral Health Impact Profile-14 (OHIP-14), respectively. Descriptive and analytical statistics was done using the SPSS 22. Analysis of variance, Kruskal‒Wallis test, and Pearson correlation test were used. P < 0.05 was considered statistically significant. Results: The prevalence of malocclusion was found to be 29.63%. Among schoolchildren, the mean OASIS scores (self-perception) and OHIP-14 scores were 14.62 ± 6.93 and 9.04 ± 8.07, respectively. There was a significant difference among government, aided, and private schoolchildren regarding “definite malocclusion” and mean OHIP scores. Overall, there was a significant weak correlation between the severity of malocclusion and OHIP scores ([r = 0.259], [P = 0.01]) as well as between the severity of malocclusion and OASIS scores ([r = 0. 192], [P = 0.02]). Conclusion: The severity of malocclusion significantly correlated with self-perception of dental esthetics and OHRQoL among schoolchildren. Hence, there is a relation between self-perception of dental esthetics and malocclusion as well as malocclusion and OHRQoL.

Azo dye degradation utilizing microorganisms has acquired significance over the last decades, owing to its beneficial properties such as rapid degradation, economic feasibility, and eco‐friendly. In this work, azo dyes (Methyl red and methylene blue) degradation efficacy using indigenous bacterial strains Pseudomonas sp. and Bacillus sp. as free and immobilized cells were investigated under in vitro batch decolorization setup was carried. Most importantly, the bacterial cells of both strains were immobilized with sodium alginate and the immobilized cells were evaluated for dye decolorization efficacy under laboratory conditions. An eco‐toxic assessment of the degraded products was done using in vitro phytotoxicity tests and in silico analyses. Among the free and immobilized cells, maximum decolorization efficacy was observed in the Pseudomonas sp. immobilized cells which exhibited 74.5% decolorization efficacy, and the maximum decolorization efficacy of free cells was 63% of was observed. Ecotoxicity of the dye‐degraded products was screened by determination of phytotoxicity. Seedlings emergence of groundnut seeds exposed to degraded dye products revealed phytotoxicity. Results indicated that degraded dye products of methyl red derived from Pseudomonas sp. showed 93.0% seedling emergence, and methylene blue‐degraded products Pseudomonas sp. degraded dye products supported 92.0% seedling emergence, respectively. Bacterial cells immobilized with biocompatible polymers can be effectively utilized in wastewater treatment without affecting the ecological components. The findings highlight the value of this work in contributing to the domain of sustainable and eco‐friendly wastewater treatment technology. [ABSTRACT FROM AUTHOR]

Background: Diabetes mellitus (DM) is a highly prevalent metabolic disorder which has life-long acute and chronic complications, constituting a huge global public health burden. The influence of diabetes on oral health is established. However, literature assessing psychosocial aspects that are necessary for managing metabolic control and oral health in diabetics is scarce. Aim: To determine the association between health locus of control and oral health among patients with Type 2 DM in Bengaluru City. Materials and Methods: A cross-sectional conducted among 300 participants with and without type 2 DM visiting government hospitals in Bengaluru City from November 2017 to September 2019. The Multidimensional Health Locus of Control (MHLC) Scale (Internal [IHLC], Powerful Others [PHLC], and Chance [CHLC]) and the WHO pro forma 2013 were used to assess health locus of control and oral health, respectively. Chi-square, Spearman's correlation, and multivariate hierarchical linear regression were applied. The statistical significance was considered at P < 0.05. Results: Diabetic participants were equivocal in their perceptions on IHLC, whereas nondiabetic participants were in agreement. Majority of diabetic and nondiabetic participants were in disagreement on PHLC. Nondiabetics were in disagreement on CHLC, whereas diabetics had elusive perception. Significantly higher proportion of diabetics had caries experience, deep pockets, and loss of attachment (LOA) than nondiabetic participants. Statistically significant association was found between health locus of control and dental caries experience, periodontitis, and denture use. Glycemic control, IHLC, and PHLC were found to be significant predictors of LOA. Conclusion: Oral health status was significantly poorer and associated with health locus of control. Glycemic control and health locus of control influenced oral health in diabetic participants.

Background: Diabetes mellitus is the most common disorder among endocrine disorders that are associated with hypomagnesemia. So far many studies have shown that Mg levels are lower in diabetic patients. According to CARDIA Study (Coronary Artery Risk Development in young Adults) there was an inverse relationship between Mg intake and the incidence of diabetes. Mg depletion may cause an insulin-resistant state, poor glycemic control and disordered lipid metabolism in diabetic patients. Furthermore, poor glycemic control in diabetic patients is a well-known risk factor for Mg depletion. Serum Mg depletion has been reported in diabetic patients who had advanced retinopathy and poor glycemic control. It has been shown that diabetic patients, hypomagnesemia has been associated with poor glycemic control and urine albumin excretion and complications Material And Methods This study was conducted for 1 year in the outpatient department of General Medicine, A. J. Institute of Medical Sciences and Research Centre, Mangalore starting from September 2019 to August 2020. Hospital based Observational study was conducted among 117 individuals. Diabetic patients were observed and serum magnesium levels were monitored. 23 patients were observed to have hypomagnesemia and the rest 92 were found to have normal serum magnesium levels. The results were analyzed using student ‘t’ test. Results The mean age in the hypomagnesemia group was 61.92 SD + .8.76, and in those with normal magnesium levels it was 60. 79 years SD + 8.90 years. In the hypomagnesemia group 52% were females and 48% were males and in those who had normal magnesium levels, 31.52%were females and 68.48% were males. 64 % in hypomagnesemia group and 12.77%, in the normal magnesium group had complications. The mean potassium in the normal magnesium group was 4.606 mEq/dl, hypomagnesemia group was 3.096 mEq/dl. The mean sodium in the normal magnesium group was 133.48 mEq/dL and in the hypomagnesemia group was 130.03 mEq/dL. Conclusion Prevalence of hypomagnesemia in diabetics is 25%. Prevalence of hypomagnesemia is significantly higher in patients with diabetic complications. Hypomagnesemia is significantly associated with glycemic control and potassium levels. No significant association was found to exist between hypomagnesemia, age and gender. [ABSTRACT FROM AUTHOR]