Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.

Leaf litter decomposition and carbon release patterns in five homegarden tree species of Kumaun Himalaya viz. Ficus palmata, Ficus auriculata, Ficus hispida, Grewia optiva and Celtis austalaris were investigated. The study was carried out for 210 days by using litter bag technique. In the current investigation, the duration needed for desertion of the original biomass of diverse leaf litter varied from 150 to 210 days and specifies a varying pattern of decomposition and carbon release among the species. Grewia optiva took the longest time to decompose (210 days) while Ficus hispida decomposed more quickly than rest of the species (150 days). The relative decomposition rate (RDR) was reported highest in Ficus hispida (0.009-0.02 g-1d-1) and lowest in Grewia optiva (0.008-0.004 g-1d-1). Carbon (%) in remaining litter was in the order: Ficus auriculata (24.4 %) >Ficus hispida (24.3%) > Celtis austaralis (19.8%) > Ficus palmata (19.7%) > Grewia optiva (19%). The relationship between percentage weight loss and time elapsed showed the significant negative correlation with carbon release pattern in all the species. Releasing nutrients into the soil through the decomposition of homegarden tree residuals is a crucial ecological function that also regulates the nutrient recycling in homegarden agroforestry practices.

An experiment was conducted to assess the effect of psychoenergetic energy in litchi as positive and negative thoughts using a simple meditation technique at ICAR-NRC on Litchi, Muzaffarpur. The plant produced 24.75 g of fruit given positive energy, while the plant with negative thought energy produced 22.12 g of fruit. The fruit and seed weight increased by 11.88% and 13.63%, respectively, due to positive energy. The number of fruit retentions increased by 23.77% due to positive energy. Anthocyanin content in pericarp was increased by 5.45% in plants given positive energy. Fruit qualities were also significantly affected by psychoenergy. TSS (Brix) was significantly increased by 13.54% in plants given positive energy as compared to negative energy, and titratable acidity was reduced by 25% due to positive energy. Ascorbic acid was also increased by 30% in plant given positive thoughts. Sun burn was reduced by 54.76% and fruit cracking by 63.64% due to energy of thought. Fruit borer infestation was reduced by 70%, and mite infestation was reduced by 90% in plants given positive energy. The psychoenergetic potential is vast, and its ability to improve crop yield and quality cannot be overstated. The hidden power of thought is being practiced by all, but mostly people do not know this power and use it in an improper manner. This is a high time when we need to practice generating powerful thoughts to change present-day agriculture and its dependents.

Proposed herein is an environment-friendly method to realize oil/water separation. Nylon mesh is exposed to atmospheric pressure plasma for surface modification, by which micro/nano structures and oxygen-containing groups are created on nylon fibers. Consequently, the functionalized mesh possesses superhydrophilicity in air and thus superoleophobicity underwater. The water pre-wetted mesh is then used to separate oil/water mixtures with the separation efficiency above 97.5% for various oil/water mixtures. Results also demonstrate that the functionalized nylon mesh has excellent recyclability and durability in terms of oil/water separation. Additionally, polyurethane sponge slice and polyester fabric are also functionalized and employed to separate oil/water mixtures efficiently, demonstrating the wide suitability of this method. This simple, green and highly efficient method overcomes a nontrivial hurdle for environmentally-safe separation of oil/water mixtures, and offers insights into the design of advanced materials for practical oil/water separation.

In response to the prevailing energy crisis, this research focuses on elevating the potential of lithium niobate (LN) thin films for advanced optoelectronic applications. Employing electron beam evaporation, films undergo precise annealing (700°C to 1100°C), revealing a structural evolution through X-ray diffraction—crystallite sizes transition from 69.34 nm (unannealed) to 47.90 nm (1100°C). Scanning electron microscopy captures the transformation from coarse grains to photonic crystal clusters, while energy dispersion X-ray analysis discloses LN's composition (97.27 wt.% oxygen, 2.73 wt.% niobium). Rutherford backscattering spectroscopy illustrates surface damage post-Helium ion implantation, proportionate to depth. UV-VIS spectrophotometry highlights a significant blue shift in the optical band gap (3.70 eV to 2.52 eV), with further reduction at 700°C (2.48 eV) and a climactic shift at 1100°C (2.68 eV). This study not only addresses the pressing energy crisis but also emphasizes the indispensable role of lithium niobate in shaping the future of optoelectronics. It provides insights into tailoring LN properties for sustainable advancements in optoelectronic devices, marking a crucial chapter in our collective journey towards energy resilience. The urgency of innovation in the face of global challenges is underscored, marking a crucial chapter in our collective journey towards energy resilience.

This paper presents a numerical method for solving a nonlinear age-structured population model based on a set of piecewise constant orthogonal functions. The block-pulse functions (BPFs) method is applied to determine the numerical solution of a non-classic type of partial differential equation with an integral boundary condition. BPFs duo to the simple structure can efficiently approximate the solution of systems with local or non-local boundary conditions. Numerical results reveal the accuracy of the proposed method even for the long term simulations.