Explore the vast range of titles available.

The field of image denoising has undergone significant advancements over the years. Recently, Convolutional Neural Networks (CNN) based denoising methods have shown remarkable performance in image denoising. Most of these adopt single-scale features, which may have limitations in denoising real-world images. Real-world noise is complex and non-Gaussian in nature. The multi-scale strategy of the Gaussian pyramid (GP) facilitates the attenuation of noise while preserving image details. Additionally, this multiscale architecture inherently reduces the data’s dimensionality, resulting in decreased computational complexity. Over the past few decades, this method has been employed for image denoising; however, its application to real-world images remains computationally challenging. In this study, we implemented the GP method for denoising X-ray, MRI, non-medical images, and SIDD datasets. Furthermore, its denoising performance is compared with the wavelet transforms (Coiflet4, Haar, Daubechies, and Symlets). Quantitatively, GP achieves a significant improvement in PSNR, SSIM, and computational complexity compared to the wavelet method. PSNR of 36.8024 dB, SSIM of 0.9428, and computational complexity of 0.0046 s have been achieved, thereby offering an effective and practical solution for real-world image applications.

Mango farming is a key economic activity in several locations across the world. Mango trees are prone to various diseases caused by viruses and pests, which can substantially impair crops and have an effect on farmers' revenue. To stop the spread of these illnesses and to lessen the crop damage they cause, early diagnosis of these diseases is essential. Growing interest has been shown in employing deep learning models to create automated disease detection systems for crops because of recent developments in machine learning. This research article includes a study on the application of ConvNeXt models for the diagnosis of pathogen and pest caused illnesses in mango plants. The study intends to investigate the variety in how these illnesses emerge on mango leaves and assess the efficiency of ConvNeXt models in identifying and categorizing them. Images of healthy mango leaves as well as the leaves with a variety of illnesses brought on by pathogens and pests are included in the dataset used in the study. In the study, deep learning models were applied to classify mango pests and pathogens. The models achieved high accuracy on both datasets, with better performance on the pathogen dataset. Larger models consistently outperformed smaller ones, indicating their ability to learn complex features. The ConvNeXtXLarge model showed the highest accuracy: 98.79% for mango pests, 100% for mango pathogens, and 99.17% for the combined dataset. This work holds significance for mango disease detection, aiding in efficient management and potential economic benefits for farmers. However, the models' performance can be influenced by dataset quality, preprocessing techniques, and hyperparameter selection.

The shade on rooftop Photovoltaic (PV) array is mainly due to the chimney on rooftop, cell towers, neighbouring building and trees etc, which cannot be avoided due to the place constraint in cities and towns. But the green energy production from solar PV modules is much more important in cities and towns to meet the increased demand of energy. In most of the cases, the static shade on PV modules is subjected to last rows or columns which are very near to the PV array boundaries. Hence, the popular PV modules connection i.e conventional Total Cross Tied (TCT) connections need to be modified to improve the generation of power during uncertainty in weather conditions. An attempt has been made on conventional TCT to improve its electrical connections for enhanced power output, reduction in power loss, optimum space and less financial requirement by omitting one PV module using the proposed Improved TCT (ITCT) connections by changing the last row of array connections only, during the installation stage itself. The performance analysis has been compared among the proposed ITCT scheme and the existing electrical configurations such as Series-Parallel, Honey-Comb, Bridge-Link, and TCT, which is validated with the mathematical analysis and MATLAB/Simulink simulations. The proposed scheme has shown better overall performance compared to the existing electrical configurations in terms of reduced number of PV modules under all the shading cases. Further, the maximum power loss has been reduced with the proposed ITCT over the conventional TCT under all shading pattern, achieved maximum power enhancement of 16.74%, and single peak power under all shading cases shows an advantage of its adaptability in real time large scale PV array.

Grid-tied solar photovoltaic systems use a PWM-VSI STATCOM to regulate active and reactive power. Due to high reactive power demand, often it has been experienced that there is a decay in reactive power supply which may cause malfunction in the load side equipments. The STATCOM balances power variations caused by solar irradiation and ensures constant DC bus voltage for efficient power conversion and optimal MPPT performance. It also provides dynamic reactive power support, balancing imbalanced loads and filtering harmonics. The modified PWM-VSI controlled by Genetic Algorithm optimized Fractional Order based STATCOM approach enhances dynamic response, improves system efficiency, and integrates with MPPT (SAR) for simultaneous reactive power compensation and extraction. The proposed system ensures grid stability during variable solar generation and outperforms the P&O MPPT controller in active and reactive power management. The proposed system uses a modified PWM-VSI STATCOM controller (FOSTATCOM) to regulate PV system voltage and current waveforms, ensuring grid stability during variable solar generation. The SAR MPPT connected SPV system tied utility grid also outperforms the P&O MPPT controller in active and reactive power management, allowing for 109.1 KW active power supply and 360.2 VAR reactive power supply by integrating modified STATCOM as compared to the P&O MPPT controller with standard PWM-VSI STATCOM which is supplying 108.1 KW and 865.3 VAR.

The aim of this article is to utilize fractional calculus for performance enhancement of nonlinear fuzzy PD + I controller. A fractional order fuzzy PD + I controller (FOFPD + I) is designed and implemented to control complex, uncertain and nonlinear robotic manipulator. FOFPD + I controller is derived from fractional order PD and fractional order I controller. The proposed control strategy has an adaptive capability due to its nonlinear gains and preserves the linear structure of fractional order PD + I controller. Further, integer-order fuzzy PD + I controller (FPD + I) and conventional PID controllers are also designed for comparative analysis. The optimum parameter values of FOFPD + I, FPD + I and PID controllers are obtained using non-dominated sorting genetic algorithm-II. The effectiveness of proposed controller is examined for reference tracking and disturbance rejection problems of robotic manipulator. The designed controllers are also validated experimentally on DC servomotor. Simulation and experimental results prove the superiority of FOFPD + I controller as compared to its integer-order equivalent and conventional PID controllers for control of robotic manipulator.

This work is focused on multi-objective optimisation of a multi-drug chemotherapy schedule for cell-cycle-specific cancer treatment under the influence of drug resistance. The acquired drug resistance to chemotherapeutic agents is incorporated into the existing compartmental model of breast cancer. Furthermore, the toxic effect of drugs on healthy cells and overall drug concentration in the patient body are also constrained in the proposed model. The objective is to determine the optimal drug schedule according to the patient’s physiological condition so that the tumour burden is minimised. A multi-objective optimisation algorithm, non-dominated sorting genetic algorithm-II (NSGA-II) is utilised to solve the problem. The obtained results are thoroughly analysed to illustrate the impact of drug resistance on the treatment. The capability of optimised schedules to deal with parametric uncertainty is also analysed. The drug schedules obtained in this work align well with the clinical standards. It is also revealed that the NSGA-II optimised drug schedule with proper rest period between successive dosages yields the minimum cancer load at the end of the treatment.

This Effect of Nano-carbon additives on mechanical performance and durability of cementitious composites in oi terms of static and dynamic modulus is studied. A Nano carbon powder was admixed in 5%, 10% and 15 % levels as a cement replacement in M20, M25 and M30 concrete grades and the mechanical properties were for curing ages of 14, 28 and 84 days. These results show that 5% Nano-carbon addition has improved static and dynamic modulus by increasing stiffness, fatigue resistance and load bearing capability. However, modulus values decreased above 10 % and 15 % replacement levels (due to agglomeration effects and non-uniform dispersion of carbon particles to matrices). A statistical analysis of modulus variations revealed lower variability at 5 % replacement, indicating good performance. Those findings confirm that at controlled dosages, Nano carbon additives improve mechanical properties and are suitable for high-performance concrete (and) bridges, pavements, and smart infrastructure. This study highlights the feasibility of using nano carbon additives to reach the performance targets in high performance concrete applications such as bridges, pavements and smart infrastructure. This research encourages sustainability through the reduced use of cement and reduction of industrial waste by using waste derived carbon materials.

There are sex differences in vulnerability and resilience to the stressors of aging and subsequent age-related cognitive decline. Cellular senescence occurs as a response to damaging or stress-inducing stimuli. The response includes a state of irreversible growth arrest, the development of a senescence-associated secretory phenotype, and the release of pro-inflammatory cytokines associated with aging and age-related diseases. Senolytics are compounds designed to eliminate senescent cells. Our recent work indicates that senolytic treatment preserves cognitive function in aging male F344 rats. The current study examined the effect of senolytic treatment on cognitive function in aging female rats. Female F344 rats (12 months) were treated with dasatinib (1.2 mg/kg) + quercetin (12 mg/kg) or ABT-263 (12 mg/kg) or vehicle for 7 months. Examination of the estrus cycle indicated that females had undergone estropause during treatment. Senolytic treatment may have increased sex differences in behavioral stress responsivity, particularly for the initial training on the cued version of the watermaze. However, pre-training on the cue task reduced stress responsivity for subsequent spatial training and all groups learned the spatial discrimination. In contrast to preserved memory observed in senolytic-treated males, all older females exhibited impaired episodic memory relative to young (6-month) females. We suggest that the senolytic treatment may not have been able to compensate for the loss of estradiol, which can act on aging mechanisms for anxiety and memory independent of cellular senescence.

In this study, electrical characteristics of MoTe2 field-effect transistors (FETs) are investigated as a function of channel thickness. The conductivity type in FETs, fabricated from exfoliated MoTe2 crystals, switched from p-type to ambipolar to n-type conduction with increasing MoTe2 channel thickness from 10.6 nm to 56.7 nm. This change in flake-thickness-dependent conducting behavior of MoTe2 FETs can be attributed to modulation of the Schottky barrier height and related bandgap alignment. Change in polarity as a function of channel thickness variation is also used for ammonia (NH3) sensing, which confirms the p- and n-type behavior of MoTe2 devices.

Background Mosquitoes are intermediate hosts for numerous disease causing organisms. Vector control is one of the most investigated strategy for the suppression of mosquito-borne diseases. Anopheles stephensi is one of the vectors of malaria parasite Plasmodium vivax . The parasite undergoes major developmental and maturation steps within the mosquito midgut and little is known about Anopheles -associated midgut microbiota. Identification and characterization of the mosquito midgut flora is likely to contribute towards better understanding of mosquito biology including longevity, reproduction and mosquito-pathogen interactions that are important to evolve strategies for vector control mechanisms. Results Lab-reared and field-collected A. stephensi male, female and larvae were screened by \"culture-dependent and culture-independent\" methods. Five 16S rRNA gene library were constructed form lab and field-caught A. stephensi mosquitoes and a total of 115 culturable isolates from both samples were analyzed further. Altogether, 68 genera were identified from midgut of adult and larval A. stephensi , 53 from field-caught and 15 from lab-reared mosquitoes. A total of 171 and 44 distinct phylotypes having 85 to 99% similarity with the closest database matches were detected among field and lab-reared A. stephensi midgut, respectively. These OTUs had a Shannon diversity index value of 1.74–2.14 for lab-reared and in the range of 2.75–3.49 for field-caught A. stephensi mosquitoes. The high species evenness values of 0.93 to 0.99 in field-collected adult and larvae midgut flora indicated the vastness of microbial diversity retrieved by these approaches. The dominant bacteria in field-caught adult male A. stephensi were uncultured Paenibacillaceae while in female and in larvae it was Serratia marcescens , on the other hand in lab-reared mosquitoes, Serratia marcescens and Cryseobacterium meninqosepticum bacteria were found to be abundant. Conclusion More than fifty percent of the phylotypes were related to uncultured class of bacteria. Interestingly, several of the bacteria identified are related to the known symbionts in other insects. Few of the isolates identified in our study are found to be novel species within the gammaproteobacteria which could not be phylogenetically placed within known classes. To the best of our knowledge, this is the first attempt to study the midgut microbiota of A. stephensi from lab-reared and field-collected adult and larvae using \"culture-dependent and independent methods\".