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.

As the shift to electric mobility intensifies, unpredictable EV charging challenges grid stability. This study proposes a multi-layered machine learning framework balancing grid optimization and user service. First, session-level prediction models estimated energy and cost; XGBoost achieved the highest energy accuracy ( ), while Random Forest best predicted cost ( ). Second, a station-level forecasting model using XGBoost demonstrated exceptional precision for daily demand ( , MAE=0.90 kWh). Finally, K-Means clustering segmented drivers, revealing a user base dominated by Heavy Energy Users (43.5%) and Occasional Visitors (38.8%). This segmentation enables Charge Point Operators to design personalized services and demand response strategies. Overall, the framework integrates prediction, forecasting, and behavioral segmentation to support scalable, data-driven decisions. Ultimately, these insights equip utility providers and operators with the necessary tools to proactively manage load congestion and optimize capital expenditure planning.

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.

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.

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.

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.

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.

Gerontological research reveals considerable interindividual variability in aging phenotypes, which has motivated research efforts to identify “aging biomarkers.” Aging biomarkers are used to calculate biological age, which are better predictors of disease risk and residual lifespan when compared to chronological age alone. Emerging evidence using the epigenetic clock as an aging biomarker supports highly reliable individualized predictions about future health. This study aimed to determine whether an epigenetic aging biomarker was associated with chronic pain in older adults (60–83 years old). A subset of participants (n = 29) in the Neuromodulatory Examination of Pain and Mobility Across the Lifespan study underwent a blood draw, demographic, psychological, cognitive, and pain assessments. We estimated Horvath’s epigenetic clock and calculated the difference between epigenetic age and chronological age that has been previously reported to predict overall mortality risk. Older individuals without chronic pain (n = 9) had significantly “younger” epigenetic age compared to those with chronic pain (n = 20, p < 0.05). Older epigenetic age was associated with greater pain during daily activities (r = 0.494, p = 0.010) and anatomical pain sites (r = 0.741, p < 0.001) but not pain frequency/duration. An older epigenetic age was also associated with higher vibratory detection thresholds (r = 0.490, p = 0.021), heat pain thresholds (r = −0.478, p = 0.028), and pressure pain thresholds at the trapezius (r = −0.571, p = 0.006) but not thermal detection, pressure pain at the quadriceps or pain inhibition (p’s > 0.05). Epigenetic aging was associated with greater emotional stability (r = −0.461, p = 0.027), conscientiousness (r = −0.549, p = 0.007), and lower extraversion (r = 0.414, p = 0.049) but not depression or affect (p’s > 0.05). Epigenetic aging was also associated with lower episodic (r = −0.698, p = 0.001) and working memory (r = −0.760, p < 0.001). Our findings suggest that chronic pain is associated with accelerated epigenetic aging in healthy, community-dwelling older individuals, and future studies with larger samples are needed to confirm our findings. An aging biomarker such as the epigenetic clock may help identify people with chronic pain at greater risk of functional decline and poorer health outcomes.

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.